The impact of unplanned school closure on children’s social contact: rapid evidence review
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Open Access
- 2 April 2020
- journal article
- review article
- Published by European Centre for Disease Control and Prevention (ECDC) in Eurosurveillance
- Vol. 25 (13), 2000188-30
- https://doi.org/10.2807/1560-7917.es.2020.25.13.2000188
Abstract
Background: Emergency school closures are often used as public health interventions during infectious disease outbreaks to minimise the spread of infection. However, if children continue mixing with others outside the home during closures, the effect of these measures may be limited. Aim: This review aimed to summarise existing literature on children’s activities and contacts made outside the home during unplanned school closures. Methods: In February 2020, we searched four databases, MEDLINE, PsycInfo, Embase and Web of Science, from inception to 5 February 2020 for papers published in English or Italian in peer-reviewed journals reporting on primary research exploring children’s social activities during unplanned school closures. Main findings were extracted. Results: A total of 3,343 citations were screened and 19 included in the review. Activities and social contacts appeared to decrease during closures, but contact remained common. All studies reported children leaving the home or being cared for by non-household members. There was some evidence that older child age (two studies) and parental disagreement (two studies) with closure were predictive of children leaving the home, and mixed evidence regarding the relationship between infection status and such. Parental agreement with closure was generally high, but some disagreed because of perceived low risk of infection and issues regarding childcare and financial impact. Conclusion: Evidence suggests that many children continue to leave home and mix with others during school closures despite public health recommendations to avoid social contact. This review of behaviour during unplanned school closures could be used to improve infectious disease modelling.Keywords
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