title: Patterns of Treatment Switching in Multiple Sclerosis Therapies in US Patients Active on Social Media: Application of Social Media Content Analysis to Health Outcomes Research.
abstract: BACKGROUND
  Social media analysis has rarely been applied to the study of specific questions in outcomes research.
  
  
  OBJECTIVE
  The aim was to test the applicability of social media analysis to outcomes research using automated listening combined with filtering and analysis of data by specialists. After validation, the process was applied to the study of patterns of treatment switching in multiple sclerosis (MS).
  
  
  METHODS
  A comprehensive listening and analysis process was developed that blended automated listening with filtering and analysis of data by life sciences-qualified analysts and physicians. The population was patients with MS from the United States. Data sources were Facebook, Twitter, blogs, and online forums. Sources were searched for mention of specific oral, injectable, and intravenous (IV) infusion treatments. The representativeness of the social media population was validated by comparison with community survey data and with data from three large US administrative claims databases: MarketScan, PharMetrics Plus, and Department of Defense.
  
  
  RESULTS
  A total of 10,260 data points were sampled for manual review: 3025 from Twitter, 3771 from Facebook, 2773 from Internet forums, and 691 from blogs. The demographics of the social media population were similar to those reported from community surveys and claims databases. Mean age was 39 (SD 11) years and 14.56% (326/2239) of the population was older than 50 years. Women, patients aged 30 to 49 years, and those diagnosed for more than 10 years were represented by more data points than other patients were. Women also accounted for a large majority (82.6%, 819/991) of reported switches. Two-fifths of switching patients had lived with their disease for more than 10 years since diagnosis. Most reported switches (55.05%, 927/1684) were from injectable to oral drugs with switches from IV therapies to orals the second largest switch (15.38%, 259/1684). Switches to oral drugs accounted for more than 80% (927/1114) of the switches away from injectable therapies. Four reasons accounted for more than 90% of all switches: severe side effects, lack of efficacy, physicians' advice, and greater ease of use. Side effects were the main reason for switches to oral or to injectable therapies and search for greater efficacy was the most important factor in switches to IV therapies. Cost of medication was the reason for switching in less than 0.5% of patients.
  
  
  CONCLUSIONS
  Social intelligence can be applied to outcomes research with power to analyze MS patients' personal experiences of treatments and to chart the most common reasons for switching between therapies.
authors.affiliation: Novartis Pharma AG, Basel, Switzerland. valery.risson@novartis.com.
authors.forename: Valéry
authors.identifier: http://orcid.org/0000-0001-9597-8797
authors.initials: V
authors.lastname: Risson
authors.forename: Deepanshu
authors.identifier: http://orcid.org/0000-0001-6127-1574
authors.initials: D
authors.lastname: Saini
authors.forename: Ian
authors.identifier: http://orcid.org/0000-0001-6755-4065
authors.initials: I
authors.lastname: Bonzani
authors.forename: Alice
authors.identifier: http://orcid.org/0000-0001-8219-3501
authors.initials: A
authors.lastname: Huisman
authors.forename: Melvin
authors.identifier: http://orcid.org/0000-0002-2870-4443
authors.initials: M
authors.lastname: Olson
mesh_terms: D000328:Adult; D000368:Aged; D057218:Blogging; D003625:Data Collection; D016208:Databases, Factual; D057915:Drug Substitution; D005260:Female; D006801:Humans; D020407:Internet; D008297:Male; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000188:drug therapy*; D017063:Outcome Assessment, Health Care; D061108:Social Media; D011795:Surveys and Questionnaires; D014481:United States; D055815:Young Adult
keywords: Internet; drug switching; multiple sclerosis; outcomes assessment