Social Media in Public Health: National Immunization Conference 2013


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The National Immunisation Conference was held in Manchester on 5th December 2013 and featured a range of topics on public health and immunization.

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Social Media in Public Health: National Immunization Conference 2013

  1. 1. What can Social Media Offer Public Health? Sarah Amani Chief Clinical Information Officer (CCIO) Twitter: @S_Amani
  2. 2. About Me
  3. 3. What is Social Media? Social media refers to the means of interactions among people in which they create, share, and exchange information and ideas in virtual communities and networks.
  4. 4. • Facebook = Friend Builder • Twitter = Broadcast & Community Builder • YouTube = Broadcast Content to the World • Flickr = Photographs to Inspire • LinkedIn = Professional Connections • Blogs = Your own online newspaper • Social Bookmarking (Digg, Stumble) = Sharing information
  5. 5. “The impact of social media on the balance of power and knowledge between patient and professional is enormously significant...” Disruptive Innovation Report, 2008
  6. 6. Implications • Majority do vaccinate & immunise themselves & their children • But many have concerns over safety • They are seeking information online & via social media • How reliable is this information?
  7. 7. The Wisdom of Crowds?
  8. 8. “A lie can travel halfway around the world while the truth is putting on its shoes.” Mark Twain
  9. 9. INSIGHT A Variety of Factors Influence Behaviour Personal factors • Knowledge and awareness – people might not have a good understanding of problem • Attitudes – view on importance • Habit – whether or not their other children had it • Perceived behavioural control – how easy/hard they think it will be • Emotions – fear of jabs, belief in probability of occurrence Social factors • Norms – how does their action fit within their peer groups/community? Local and wider environment • Travelling to the GPs • Pre-existing condition • Attending with multiple children • Non-English speakers
  10. 10. Ajzen Theory of Planned Behaviour Behavioural Beliefs “This act will (will not) have positive consequences.” Subjective Norms “People who are important to me think I should (not do) this.” Perceived Behavioural Control “It will be easy (difficult) for me to do this.” Behavioural Intention “I intend (do not intend) to do this.” Behaviour “I actually do (do not do) this.”
  11. 11. Tracking Anti-Vaccination Sentiment in Europe Using Social Media. Unicef, April 2013
  12. 12. #vaccineswork The main findings were: •In all four languages, blogs were the most frequently used channel for posting anti- vaccine content in social media (86 per cent in Romanian, 85 per cent in Polish, 65 per cent in Russian and 47 per cent in English). •Facebook was the second largest channel among all four languages. Twitter was the second largest channel in Russian, with 24 per cent of total volume. •While conversations on forums only made up 2 per cent of total conversations, they accounted for 25 per cent of interactions. •The data skews towards female audiences on issues such as developmental disabilities (59 per cent), chemical and toxins (56 per cent), and side effects (54 per cent). •Men focused on arguments around conspiracy theory (63 per cent) and religious/ethical beliefs (58 per cent). •Participants discussing anti-vaccination sentiments are approximately 56 per cent female and 44 per cent male.
  13. 13. 1. Positive: A positive sentiment means the author is likely to get the influenza A (H1N1) vaccine e.g. “Off to get swine flu vaccinated before work.” 2. Negative: A negative sentiment means the author is unlikely to get the influenza A (H1N1) vaccine e.g “What Can You Do To Resist The U.S. H1N1 "Vaccination" Program? Help Get Word Out. The H1N1 "Vaccine" Is DIRTY. DontGetIt.” 3. Neutral: No clear sentiment can be detected e.g. “The Health Department will be offering the seasonal flu vaccine for children 6 months - 19 yrs. of age starting on Monday, Nov. 16.” 4. Irrelevant: The tweet is not clearly about the influenza A(H1N1) vaccine. “Filipino discovers new vaccine against malaria that 'treats' the mosquitoes, too!”
  14. 14. Results •477,768 collected tweets, •318,379 were classified as relevant to the influenza A (H1N1) vaccine. •Of those, 255,828 were classified as neutral •26,667 as negative, •35,884 as positive.
  15. 15. Figure 1. (A) Total number of negative (red), positive (green), and neutral (blue) tweets relating to influenza A(H1N1) vaccination during the Fall wave of the 2009 pandemic. Salathé M, Khandelwal S (2011) Assessing Vaccination Sentiments with Online Social Media: Implications for Infectious Disease Dynamics and Control. PLoS Comput Biol 7(10): e1002199. doi:10.1371/journal.pcbi.1002199
  16. 16. Main Limitations 1. Observational study - can't exclude other confounding factors (e.g. vaccine supply) 2. Users of online social media might not be a representative sample of the population; 3. Messages may be interpreted differently by different users, and sentiment analysis is not 100% accurate
  17. 17. "These results could be used strategically to develop public-health initiatives," Salathé explained. "For example, targeted campaigns could be designed according to which region needs more prevention education. Such data also could be used to predict how many doses of a vaccine will be required in a particular area."
  18. 18. East Sussex County Council & NHS Public health behaviour change social media case study Boosting the uptake of MMR vaccination
  19. 19. Creating a Social Movement: Protecting & Improving the Lives of Children in Sussex t
  20. 20. ‘We drip-fed messages and stories about MMR without becoming repetitive’
  21. 21. So What? • We MUST all play our part • We NEED to improve digital literacy in the NHS • We MUST confront misinformation • We CAN increase immunisation
  22. 22. #NIC24
  23. 23. #NIC24
  24. 24. Thank You Any Questions? Any Questions? @S_Amani