Determinants of adults' intention to vaccinate against pandemic swine flu
School of Social Sciences, Brunel University, Uxbridge, UK
BMC Public Health 2011, 11:15 doi:10.1186/1471-2458-11-15Published: 6 January 2011
Vaccination is one of the cornerstones of controlling an influenza pandemic. To optimise vaccination rates in the general population, ways of identifying determinants that influence decisions to have or not to have a vaccination need to be understood. Therefore, this study aimed to predict intention to have a swine influenza vaccination in an adult population in the UK. An extension of the Theory of Planned Behaviour provided the theoretical framework for the study.
Three hundred and sixty two adults from the UK, who were not in vaccination priority groups, completed either an online (n = 306) or pen and paper (n = 56) questionnaire. Data were collected from 30th October 2009, just after swine flu vaccination became available in the UK, and concluded on 31st December 2009. The main outcome of interest was future swine flu vaccination intentions.
The extended Theory of Planned Behaviour predicted 60% of adults' intention to have a swine flu vaccination with attitude, subjective norm, perceived control, anticipating feelings of regret (the impact of missing a vaccination opportunity), intention to have a seasonal vaccine this year, one perceived barrier: "I cannot be bothered to get a swine flu vaccination" and two perceived benefits: "vaccination decreases my chance of getting swine flu or its complications" and "if I get vaccinated for swine flu, I will decrease the frequency of having to consult my doctor," being significant predictors of intention. Black British were less likely to intend to have a vaccination compared to Asian or White respondents.
Theoretical frameworks which identify determinants that influence decisions to have a pandemic influenza vaccination are useful. The implications of this research are discussed with a view to maximising any future pandemic influenza vaccination uptake using theoretically-driven applications.