Swamy Venuturupalli1, Amit Kumar2, Alden Bunyan3, Nikhil Davuluri2, Natalie Fortune2, Katja Reuter4. 1. Cedars-Sinai Medical Center, Los Angeles, CA, United States; David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States. 2. Cedars-Sinai Medical Center, Los Angeles, CA, United States. 3. Borra College of Health Sciences, Dominican University, IL, United States. 4. Department of Public Health and Preventive Medicine, SUNY Upstate Medical University, Syracuse, NY, United States; Southern California Clinical and Translational Science Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States.
Abstract
OBJECTIVE: Patient communities use social media for peer support and information seeking. This study assessed the feasibility of using public patient-generated health data (PGHD) from the social network Twitter to identify diverse lupus patients and gather their perspectives about disease symptoms and medications. METHODS: We extracted public lupus-related Twitter messages (N=47,715 tweets) in English posted by users (N=8,446) in the United States between September 1, 2017, and October 31, 2018. We analyzed the data to describe lupus patients and the expressed themes (symptoms and medications). Two independent coders analyzed the data; Cohen's Kappa coefficient was used to ensure interrater reliability. Differences in symptom and medication expressions were analyzed using two-tailed Z tests and a combination of one-way ANOVA tests and unpaired t-tests. RESULTS: We found that lupus patients on Twitter are diverse in gender and race: about one-third (34.64%, 62/179) were persons of color (POC), and 85.47% were female. The expressed disease symptoms and medications varied significantly by gender and race. Much of our findings correlated with documented clinical observations, e.g., expressions of general pain (8.39%, 709/8,446), flares (6.05%, 511/8,446), and fatigue (4.18%, 353/8,446). However, our data also revealed less well-known patient observations, e.g., possible racial disparities within ocular manifestations of lupus. CONCLUSION: Our results indicate that social media surveillance can provide valuable lupus patient perspective data of clinical relevance. The medical community has the opportunity to harness this information to inform the patient-centered care within underrepresented patient groups such as POC. This article is protected by copyright. All rights reserved.
OBJECTIVE: Patient communities use social media for peer support and information seeking. This study assessed the feasibility of using public patient-generated health data (PGHD) from the social network Twitter to identify diverse lupus patients and gather their perspectives about disease symptoms and medications. METHODS: We extracted public lupus-related Twitter messages (N=47,715 tweets) in English posted by users (N=8,446) in the United States between September 1, 2017, and October 31, 2018. We analyzed the data to describe lupus patients and the expressed themes (symptoms and medications). Two independent coders analyzed the data; Cohen's Kappa coefficient was used to ensure interrater reliability. Differences in symptom and medication expressions were analyzed using two-tailed Z tests and a combination of one-way ANOVA tests and unpaired t-tests. RESULTS: We found that lupus patients on Twitter are diverse in gender and race: about one-third (34.64%, 62/179) were persons of color (POC), and 85.47% were female. The expressed disease symptoms and medications varied significantly by gender and race. Much of our findings correlated with documented clinical observations, e.g., expressions of general pain (8.39%, 709/8,446), flares (6.05%, 511/8,446), and fatigue (4.18%, 353/8,446). However, our data also revealed less well-known patient observations, e.g., possible racial disparities within ocular manifestations of lupus. CONCLUSION: Our results indicate that social media surveillance can provide valuable lupus patient perspective data of clinical relevance. The medical community has the opportunity to harness this information to inform the patient-centered care within underrepresented patient groups such as POC. This article is protected by copyright. All rights reserved.
Authors: Alan Oglesby; Caroline Korves; François Laliberté; Gregory Dennis; Sapna Rao; Ellison Dial Suthoff; Robert Wei; Mei Sheng Duh Journal: Appl Health Econ Health Policy Date: 2014-04 Impact factor: 2.561