title: Seasonal Influenza Vaccination amongst Medical Students: A Social Network Analysis Based on a Cross-Sectional Study.
abstract: BACKGROUND
  The Chief Medical Officer for England recommends that healthcare workers have a seasonal influenza vaccination in an attempt to protect both patients and NHS staff. Despite this, many healthcare workers do not have a seasonal influenza vaccination. Social network analysis is a well-established research approach that looks at individuals in the context of their social connections. We examine the effects of social networks on influenza vaccination decision and disease dynamics.
  
  
  METHODS
  We used a social network analysis approach to look at vaccination distribution within the network of the Lancaster Medical School students and combined these data with the students' beliefs about vaccination behaviours. We then developed a model which simulated influenza outbreaks to study the effects of preferentially vaccinating individuals within this network.
  
  
  RESULTS
  Of the 253 eligible students, 217 (86%) provided relational data, and 65% of responders had received a seasonal influenza vaccination. Students who were vaccinated were more likely to think other medical students were vaccinated. However, there was no clustering of vaccinated individuals within the medical student social network. The influenza simulation model demonstrated that vaccination of well-connected individuals may have a disproportional effect on disease dynamics.
  
  
  CONCLUSIONS
  This medical student population exhibited vaccination coverage levels similar to those seen in other healthcare groups but below recommendations. However, in this population, a lack of vaccination clustering might provide natural protection from influenza outbreaks. An individual student's perception of the vaccination coverage amongst their peers appears to correlate with their own decision to vaccinate, but the directionality of this relationship is not clear. When looking at the spread of disease within a population it is important to include social structures alongside vaccination data. Social networks influence disease epidemiology and vaccination campaigns designed with information from social networks could be a future target for policy makers.
authors.affiliation: Department of Health and Medicine, Lancaster University, Lancaster, Lancashire, United Kingdom.
authors.forename: Rhiannon
authors.initials: R
authors.lastname: Edge
authors.affiliation: Department of Health and Medicine, Lancaster University, Lancaster, Lancashire, United Kingdom.
authors.forename: Joseph
authors.initials: J
authors.lastname: Heath
authors.affiliation: Department of Health and Medicine, Lancaster University, Lancaster, Lancashire, United Kingdom.
authors.forename: Barry
authors.initials: B
authors.lastname: Rowlingson
authors.affiliation: Department of Health and Medicine, Lancaster University, Lancaster, Lancashire, United Kingdom.
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Keegan
authors.affiliation: Department of Health and Medicine, Lancaster University, Lancaster, Lancashire, United Kingdom.
authors.forename: Rachel
authors.initials: R
authors.lastname: Isba
mesh_terms: D000293:Adolescent; D000328:Adult; D003198:Computer Simulation; D003430:Cross-Sectional Studies; D004196:Disease Outbreaks / Q000517:prevention & control; D006801:Humans; D007252:Influenza Vaccines; D007251:Influenza, Human / Q000517:prevention & control*; D008954:Models, Biological; D009975:Orthomyxoviridae / Q000276:immunology*; D060756:Social Networking; D013337:Students, Medical; D014611:Vaccination / Q000706:statistics & numerical data; D055815:Young Adult
chemical_list: D007252:Influenza Vaccines