ArticleArthritis research & therapy2017
Social media for arthritis-related comparative effectiveness and safety research and the impact of direct-to-consumer advertising.
Article in Arthritis research & therapy, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 28 citations in OpenAlex.
- Advancements in Herpes Zoster Diagnosis, Treatment, and Management: Systematic Review of Artificial Intelligence Applications.Journal of medical Internet research · 2025Pooled it
- A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data.International journal of medical informatics · 2019Pooled it
- Natural language processing to enhance rheumatoid arthritis care in clinical studies: a scoping review of applications, data, approaches, challenges and future directions.Rheumatology international · 2026Article
- Current application, possibilities, and challenges of artificial intelligence in the management of rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis.Therapeutic advances in musculoskeletal disease · 2025Review
- Pharmacovigilance in the digital age: gaining insight from social media data.Experimental biology and medicine (Maywood, N.J.) · 2025Review
- Artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases: advancements, challenges, and future perspectives.Frontiers in immunology · 2024Review
- An industry perspective on the use of machine learning in drug and vaccine safety.Frontiers in drug safety and regulation · 2023Article
- Influenza and Measles-MMR: two case study of the trend and impact of vaccine-related Twitter posts in Spanish during 2015-2018.Human vaccines & immunotherapeutics · 2022Article
- Inflammatory bowel disease integral care units: Evaluation of a nationwide quality certification programme. The GETECCU experience.United European gastroenterology journal · 2021Article
- Tweets by People With Arthritis During the COVID-19 Pandemic: Content and Sentiment Analysis.Journal of medical Internet research · 2020Article
- Social Media as Tools to Study Dietary Habits of Patients with Rheumatic Diseases: Learning from Relevant Work on Inflammatory Bowel Diseases.Mediterranean journal of rheumatology · 2020Article
- Using Twitter to Understand the Human Bowel Disease Community: Exploratory Analysis of Key Topics.Journal of medical Internet research · 2019Article
- Establishing a Framework for the Use of Social Media in Pharmacovigilance in Europe.Drug safety · 2019Article
- Mining social media data to assess the risk of skin and soft tissue infections from allergen immunotherapy.The Journal of allergy and clinical immunology · 2019Article
- A comparison of information sharing behaviours across 379 health conditions on Twitter.International journal of public health · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.