Mapping information exposure on social media to explain differences in HPV vaccine coverage in the United States

Adam G Dunn; Didi Surian; Julie Leask; Aditi Dey; Kenneth D Mandl; Enrico Coiera

doi:10.1016/j.vaccine.2017.04.060

Mapping information exposure on social media to explain differences in HPV vaccine coverage in the United States

Vaccine. 2017 May 25;35(23):3033-3040. doi: 10.1016/j.vaccine.2017.04.060. Epub 2017 Apr 29.

Authors

Adam G Dunn¹, Didi Surian², Julie Leask³, Aditi Dey⁴, Kenneth D Mandl⁵, Enrico Coiera²

Affiliations

¹ Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia. Electronic address: adam.dunn@mq.edu.au.
² Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia.
³ School of Public Health and Sydney Nursing School, The University of Sydney, Sydney, NSW 2006, Australia.
⁴ National Centre for Immunisation Research and Surveillance, The Children's Hospital at Westmead, Sydney 2145, NSW, Australia.
⁵ Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02115, United States; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States.

PMID: 28461067
DOI: 10.1016/j.vaccine.2017.04.060

Abstract

Background: Together with access, acceptance of vaccines affects human papillomavirus (HPV) vaccine coverage, yet little is known about media's role. Our aim was to determine whether measures of information exposure derived from Twitter could be used to explain differences in coverage in the United States.

Methods: We conducted an analysis of exposure to information about HPV vaccines on Twitter, derived from 273.8 million exposures to 258,418 tweets posted between 1 October 2013 and 30 October 2015. Tweets were classified by topic using machine learning methods. Proportional exposure to each topic was used to construct multivariable models for predicting state-level HPV vaccine coverage, and compared to multivariable models constructed using socioeconomic factors: poverty, education, and insurance. Outcome measures included correlations between coverage and the individual topics and socioeconomic factors; and differences in the predictive performance of the multivariable models.

Results: Topics corresponding to media controversies were most closely correlated with coverage (both positively and negatively); education and insurance were highest among socioeconomic indicators. Measures of information exposure explained 68% of the variance in one dose 2015 HPV vaccine coverage in females (males: 63%). In comparison, models based on socioeconomic factors explained 42% of the variance in females (males: 40%).

Conclusions: Measures of information exposure derived from Twitter explained differences in coverage that were not explained by socioeconomic factors. Vaccine coverage was lower in states where safety concerns, misinformation, and conspiracies made up higher proportions of exposures, suggesting that negative representations of vaccines in the media may reflect or influence vaccine acceptance.

Keywords: Acceptability; Content analysis; Human papillomavirus vaccine; Immunization coverage; Social media; Vaccine refusal.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Female
Humans
Machine Learning
Male
Papillomavirus Infections / prevention & control*
Papillomavirus Vaccines / administration & dosage*
Patient Acceptance of Health Care
Social Media*
Socioeconomic Factors
United States
Vaccination Coverage*
Vaccination Refusal

Substances

Papillomavirus Vaccines