Social media for arthritis-related comparative effectiveness and safety research and the impact of direct-to-consumer advertising

Abstract

BackgroundSocial media may complement traditional data sources to answer comparative effectiveness/safety questions after medication licensure.MethodsThe Treato platform was used to analyze all publicly available social media data including Facebook, blogs, and discussion boards for posts mentioning inflammatory arthritis (e.g. rheumatoid, psoriatic). Safety events were self-reported by patients and mapped to medical ontologies, resolving synonyms. Disease and symptom-related treatment indications were manually redacted. The units of analysis were unique terms in posts. Pre-specified conditions (e.g. herpes zoster (HZ)) were selected based upon safety signals from clinical trials and reported as pairwise odds ratios (ORs); drugs were compared with Fisher’s exact test. Empirically identified events were analyzed using disproportionality analysis and reported as relative reporting ratios (RRRs). The accuracy of a natural language processing (NLP) classifier to identify cases of shingles associated with arthritis medications was assessed.ResultsAs of October 2015, there were 785,656 arthritis-related posts. Posts were predominantly US posts (75%) from patient authors (87%) under 40 years of age (61%). For HZ posts (n = 1815), ORs were significantly increased with tofacitinib versus other rheumatoid arthritis therapies. ORs for mentions of perforated bowel (n = 13) were higher with tocilizumab versus other therapies. RRRs associated with tofacitinib were highest in conditions related to baldness and hair regrowth, infections and cancer. The NLP classifier had a positive predictive value of 91% to identify HZ. There was a threefold increase in posts following television direct-to-consumer advertisement (p = 0.04); posts expressing medication safety concerns were significantly more frequent than favorable posts.ConclusionSocial media is a challenging yet promising data source that may complement traditional approaches for comparative effectiveness research for new medications.

Mining social media data to assess the risk of skin and soft tissue infections from allergen immunotherapy

“…As such, we used advanced informatics methods on large amounts of social media data over 5 years to detect rare signals not easily identifiable in clinical studies. [27][28][29] Our methods identified a few reports of possible SSTIs from AIT and influenza vaccine, data that support that both procedures pose no meaningful infection risk. The frequency with which we observed AIT-related posts that suggest a potential associated SSTI and the frequency with which we identified influenza vaccination-related posts that suggest a potential associated SSTI were almost identical, with overlapping CIs.…”

Establishing a Framework for the Use of Social Media in Pharmacovigilance in Europe

“…Taking into account the potential data richness of social media, especially in areas that are not necessarily covered through spontaneous reporting, Harpaz et al [ 63 ] have highlighted the potential of social media for public health monitoring due to the availability of large amounts of data that are “internet-based, patient-generated, unsolicited, and up-to-date” [ 63 ]. As stated by Curtis et al [ 64 ], there are many examples where social media have been studied for a variety of health conditions, including cancer [ 65 , 66 ], diabetes [ 67 , 68 ] and obesity [ 69 ].…”

“…Examples are the evaluation of patterns of the usage of medicines, including the potential for off-label use, lack of efficacy or use of medicines during pregnancy, to measure the effectiveness of risk management measures or to perform health outcome assessments. Curtis et al [ 64 ] studied the use of data gathered from social media to complement traditional data sources to answer questions regarding comparative effectiveness and safety. To do this, they analysed publicly available social media data including Facebook, blogs and discussion boards for posts mentioning inflammatory arthritis (e.g.…”

A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data

“…For example, Curtis et al used NLP to analyze social media data for posts mentioning inflammatory arthritis. In their study, all symptom information was manually extracted and then analyzed using disproportionality methods 42 . These methods are used to identify statistical associations between products, in this case medications, and specific events according to the FDA Adverse Event Reporting System.…”