1. 1 PAREXEL International, LLC; 2 GlaxoSmthKline ; 3 Blue Zone Industries, Inc. Safety Assessment and Selection Bias: Who uses social media to communicate about medications? Rachael L DiSantostefano 1, Jeffery L. Painter 2, Michele Thomas 3, Greg Powell 2 Poster No. 961 ●Increasingly, patients and caregivers use social media to share health and medication experiences, which may be useful for safety surveillance [1,2]. ●To describe patient characteristics among individuals using social media to communicate about 15 GSK medicines of interest and to examine a blinded subset of these medicines. Total Posts ●There were 15,168 posts with the 15 medications of interest that were manually adjudicated (n=7,916 proto-AEs, n=7,252 mentions) [Figure 1]. ●Adjudicated posts were re-classified by proto-AE status to adjust any misclassification from the initial processing algorithm (n=4,447 proto-AEs, n=10,712 mentions) [Figure 1]. Most adjudicated posts were not proto-AEs. ●Across all medications combined [Table 1], the majority of posts were –from Twitter (61%) vs. Facebook (39%) –were mentions (71%) vs. proto-AEs (29%) ●These trends held across the blinded subset of medications shown [Table 1]. TwitterFacebook Medications Total Posts Proto-AEsMentionsTotalProto-AEsMentionsTotal Total Posts (%) All 15 Medicines 15,168 2,317 (15) 6,910 (46) 9,227 2,130 (14) 3,811 (25) 5,941 Product 1 3,052 681 (22) 898 (29) 1,579 768 (25) 705 (23) 1,473 Product 2 2,123 426 (20) 938 (44) 1,364 294 (14) 465 (22) 759 Product 3592 100 (17) 265 (45) 365 61 (10) 166 (28) 227 Product 4 55 3 (5) 40 (73) 43 3 (5) 9 (16) 12 Product 5 153 15 (10) 126 (82) 141 5 (3) 7 (5) 12 Aims Methods Results Presented at the 31st International Conference on Pharmacoepidemiology & Therapeutic Risk Management, Boston, MA USA, August 22-26, 2015 Total posts are from all 15 medications; however, a subset of 5 blinded medications is also shown. Row percentages may not add to 100 due to rounding. Table 1. Posts of Interest by Source and Type Availability of Subject Characteristics by Data Source ●Overall, the GSK Safety database of spontaneous adverse events was more complete on subject characteristics than social media sources [Figure 2]. –Comparing social media sources, subject characteristics were more readily available in Facebook posts than Twitter. –The availability of subject characteristics did not vary appreciably by Proto-AE status within each social media source [not shown]. –Additional subject characteristics (e.g., race, occupation, etc.) were available for ≤1% of social media posts. Availability of Subject Characteristics by Medication ●Overall, gender and indication were more available than age in the specific subset of medications shown, just as observed in the data across all 15 medications [Figure 3]. However, there was some variation by medication. –Some medications chosen have more than one indication and/or are indicated in a specific age group or gender. –The reasons for information being more complete for some medicines relative to others is not known. ●In this 1-year study of social media medication posts, the ability to assess selection bias was limited by incomplete or missing subject characteristics. ●The availability of subject characteristics varied by social media source and medication, and it was less complete than spontaneous adverse event data from GSK’s internal safety database. ●More research is needed to better understand the generalizability of safety data reported in social media. Conclusions References 1.Harris JK, et al. CDC. Health department use of social media to identify foodborne illness - Chicago, Illinois, 2013-2014. MMWR. 2014 Aug 15;63(32):681-5. 2.Dyar OJ, et al. What makes people talk about antibiotics on social media? A retrospective analysis of Twitter use. J Antimicrob Chemother. 2014 Sep;69(9):2568-72. Acknowledgements ●The presenting author, Rachael L. DiSantostefano, was employed by GSK during the conduct of the study and by PAREXEL during the completion of the manuscript; She owns shares in GSK. ●Other co-authors are current employees and shareholders (GP, JP) of GSK or contract employees (MT) of GSK. ●This study was funded by GSK. ● De-identified social media posts from Facebook and Twitter were extracted for 15 GSK medications of interest for a one year period (1Sept2013-31Aug2014). ● Data vocabulary was standardized –medical conditions: vernacular terms mapped to MedDRA preferred and lower level terms –medications: custom curated vernacular dictionary mapped to medications ●Data were cleaned to remove spam and adjust code lists from the initial broad search criteria. ●Adverse event determination –Potential adverse events (Proto-AEs) or mentions (not Proto-AEs) were determined using proprietary NLP (natural language processing) methods provided by MedWatcher.™ –All Proto-AEs and a proportional stratified random sample of mentions were manually reviewed by healthcare professionals. –Final determination of Proto-AE or non-Proto-AE status of individual posts were manually adjudicated and re-classified as necessary for reporting. ●Available characteristics (age, gender, indication, etc.) were summarized by adjudicated Proto-AE status and social media source; availability was compared across medications and with GSK’s spontaneous safety database. Figure 1. Consort Diagram Figure 3. Attributes Available by Medication & Source FB= Facebook Tw=Twitter Figure 2. Attributes Available by Data Source Proto-AEs Mentions Proto-AE 100% sample (n=8,202) Mentions sample from 73,098 (n=8,361) Original posts (n=212,635) Post-processed posts (N=81,300) English only (n=7,852) Correct medication (n=7,916) Correct medication (n=7,252) Adjudicated as Mention (n=10,712) English only (n=8,022) Adjudicated as Proto-AE (n=4,447) Manual adjudication