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Eames, KT; Tilston, NL; White, PJ; Adams, E; Edmunds, WJ (2010)
Publisher: NIHR Health Technology Assessment Programme
Languages: English
Types: Article
BACKGROUND: Mathematical models, based on data describing normal patterns of social mixing, are used to understand epidemics in order to predict patterns of disease spread and plan interventions and responses. However, individuals who are ill show behavioural changes that affect their social mixing patterns and predictive models should take into account these changes if they are to be effective. OBJECTIVES: To describe and quantify the changes in (1) social contact behaviour experienced by individuals when they are ill with pandemic H1N1 influenza (swine flu) and (2) mixing patterns of school children that take place as a result of swine flu-related school closures. METHODS: For the first part of the study, a self-completed questionnaire-based study was carried out in the autumn/winter of 2009-10. The study population was individuals who had been diagnosed with swine flu and who received a swine flu antiviral prescription from an antiviral distribution centre (ADC). It consisted of an initial survey to be filled in when participants were symptomatic with swine flu and a follow-up survey to be filled in when they had recovered. Each part of the questionnaire had two sections: patient details and a contact diary. The second part of the study was adapted to quantify the difference in mixing patterns of pupils between the school term and the half-term holiday as school closures did not occur during the study period. Eight schools participated and questionnaire packs were distributed to them, containing two surveys: one to be filled in during the school term and one during the spring half-term holiday. RESULTS: For the patient study, approximately 3800 surveys were distributed by 31 ADCs. Overall, 317 responses to the initial survey were received and 179 participants returned the follow-up survey. For all types of a contact, except contacts made at home, there were highly significant differences in contact behaviour (Wilcoxon signed-rank test, p < 0.001). Individuals made substantially fewer contacts when they were ill than when they were well. Analysis showed that returning to work was the most significant predictor of increased numbers of contacts. Also, the greater the change in the number of symptoms reported, the greater the change in the number of contacts. For the school study, approximately 1100 questionnaire packs were distributed and 134 responses were received, with 119 paired contact diaries. Pupils reported on average 18.51 contacts each day during term time and 9.24 during the half-term holiday - a reduction of over 50% and a highly significant change (Wilcoxon signed-rank test, p < 0.0001). CONCLUSIONS: The evidence from this study suggests that ill individuals make substantial changes to their social contact patterns. These changes are strongly linked to absence from work and the severity of the reported illness. Epidemiological modellers should therefore consider the implications of illness-related behavioural changes on model predictions. Future studies to measure the extent of behavioural change in a broader cross-section of infected cases could be valuable, along with more detailed studies of the social contact patterns of school children, focusing on differences between school terms and school holidays.

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