Complementing the US Food and Drug Administration Adverse Event Reporting System With Adverse Drug Reaction Reporting From Social Media: Comparative Analysis

Background: Adverse drug reactions (ADRs) can occur any time someone uses a medication. ADRs are systematically tracked and cataloged, with varying degrees of success, in order to better understand their etiology and develop methods of prevention. The US Food and Drug Administration (FDA) has developed the FDA Adverse Event Reporting System (FAERS) for this purpose. FAERS collects information from myriad sources, but the primary reporters have traditionally been medical professionals and pharmacovigilance data from manufacturers. Recent studies suggest that information shared publicly on social media platforms related to medication use could be of benefit in complementing FAERS data in order to have a richer picture of how medications are actually being used and the experiences people are having across large populations. Objective: The aim of this study is to validate the accuracy and precision of social media methodology and conduct evaluations of Twitter ADR reporting for commonly used pharmaceutical agents. Methods: ADR data from the 10 most prescribed medications according to pharmacy claims data were collected from both FAERS and Twitter. In order to obtain data from FAERS, the SafeRx database, a curated collection of FAERS data, was used to collect data from March 1, 2016, to March 31, 2017. Twitter data were manually scraped during the same time period to extract similar data using an algorithm designed to minimize noise and false signals in social media data. Results: A total of 40,539 FAERS ADR reports were obtained via SafeRx and more than 40,000 tweets containing the drug names were obtained from Twitter\u27s Advanced Search engine. While the FAERS data were specific to ADRs, the Twitter data were more limited. Only hydrocodone/acetaminophen, prednisone, amoxicillin, gabapentin, and metformin had a sufficient volume of ADR content for review and comparison. For metformin, diarrhea was the side effect that resulted in no difference between the two platforms (P=.30). For hydrocodone/acetaminophen, ineffectiveness as an ADR that resulted in no difference (P=.60). For gabapentin, there were no differences in terms of the ADRs ineffectiveness and fatigue (P=.15 and P=.67, respectively). For amoxicillin, hypersensitivity, nausea, and rash shared similar profiles between platforms (P=.35, P=.05, and P=.31, respectively). Conclusions: FAERS and Twitter shared similarities in types of data reported and a few unique items to each data set as well. The use of Twitter as an ADR pharmacovigilance platform should continue to be studied as a unique and complementary source of information rather than a validation tool of existing ADR databases

Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning

Background: There is a limited amount of data on the safety profile of the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V). Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs. Objective: We aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs. Methods: We collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multilabel classifications using the deep neural language model Bidirectional Encoder Representations from Transformers (BERT) “DeepPavlov,” which was pretrained on a Russian language corpus and applied to the Telegram messages. The resulting area under the curve score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea. Results: Telegram users complained mostly about pain (5461/11,515, 47.43%), fever (5363/11,515, 46.57%), fatigue (3862/11,515, 33.54%), and headache (2855/11,515, 24.79%). Women reported more AEs than men (1.2-fold, P<.001). In addition, there were more AEs from the first dose than from the second dose (1.1-fold, P<.001), and the number of AEs decreased with age (β=.05 per year, P<.001). The results also showed that Sputnik V AEs were more similar to other vector vaccines (132 units) than with messenger RNA vaccines (241 units) according to the average Euclidean distance between the vectors of AE frequencies. Elderly Telegram users reported significantly more (5.6-fold on average) systemic AEs than their peers, according to the results of the phase 3 clinical trials published in The Lancet. However, the AEs reported in Telegram posts were consistent (Pearson correlation r=0.94, P=.02) with those reported in the Argentinian postmarketing AE registry. Conclusions: After the Sputnik V vaccination, Russian Telegram users reported mostly pain, fever, and fatigue. The Sputnik V AE profile was comparable with other vector COVID-19 vaccines. Discussion on social media could provide meaningful information about the AE profile of novel vaccines

A CONCEPTUAL MODEL TO PROTECT BRAND REPUTATION FACING ” FAKE NEWS”

openThis study aims to explore the nature and impact of Fake News on brands and their customers, identify different categories of Fake News, and propose a conceptual model for companies to protect their brand reputation and mitigate the effects of Fake News. The research focuses on the managerial approaches and methodologies that brands can adopt to counter Fake News and safeguard their reputation in the digital era. A comprehensive literature review examines existing studies on Fake News, brand management, and the impact of Fake News on brands and consumers, highlighting the gaps in the literature. The review defines and categorizes Fake News, explores techniques for detecting and mitigating it, and investigates the relationship between Fake News and brand management. The findings reveal a lack of research on the managerial strategies for brands to tackle Fake News effectively. The study emphasizes the importance of developing proactive measures to detect and counter Fake News, as well as building resilience against Fake News attacks. By addressing these gaps, the study aims to contribute to the development of effective strategies for brands to navigate the challenges posed by Fake News in the digital media landscape.This study aims to explore the nature and impact of Fake News on brands and their customers, identify different categories of Fake News, and propose a conceptual model for companies to protect their brand reputation and mitigate the effects of Fake News. The research focuses on the managerial approaches and methodologies that brands can adopt to counter Fake News and safeguard their reputation in the digital era. A comprehensive literature review examines existing studies on Fake News, brand management, and the impact of Fake News on brands and consumers, highlighting the gaps in the literature. The review defines and categorizes Fake News, explores techniques for detecting and mitigating it, and investigates the relationship between Fake News and brand management. The findings reveal a lack of research on the managerial strategies for brands to tackle Fake News effectively. The study emphasizes the importance of developing proactive measures to detect and counter Fake News, as well as building resilience against Fake News attacks. By addressing these gaps, the study aims to contribute to the development of effective strategies for brands to navigate the challenges posed by Fake News in the digital media landscape

UNDERSTANDING THE IMPACT OF INSTAGRAM SPONSORED ADS: MESSAGE EXPLICITNESS AND MODERATING FACTORS ON CONSUMER PERCEPTION AND BEHAVIOR

This experimental study aims to explore how Instagram sponsored advertisements impact consumer perception and behaviors, focusing on the overarching Persuasion Knowledge Model (PKM) theory. The study consists of two distinct experiments, each examining different moderators while maintaining a central emphasis on message explicitness and its interaction with other variables. In Study 1, participants were exposed to Instagram sponsored ads featuring different levels of message explicitness (explicit vs. implicit) and varying product types (utilitarian vs. hedonic). The main outcome variable assessed was immediate purchase intent. The mediators, persuasion knowledge, and perceived deceptiveness, were also analyzed to understand their impact on the correlation between message explicitness, product type, and purchase intent. The results supported the impact of perceived deceptiveness on immediate purchase intent. In Study 2, the focus remained on message explicitness (explicit vs. implicit), but the moderator shifted to ad skepticism, a continuous variable. Like the first study, the analysis included mediation by persuasion knowledge and perceived deceptiveness. The findings revealed a noteworthy difference between explicit and implicit messages concerning perceived deceptiveness. Both studies employed random participant assignment to distinct experimental conditions to ensure unbiased outcomes. Data collection occurred through online surveys, and a total of 298 participants took part in the study. The discoveries from this dissertation furnish valuable insights into the efficacy of Instagram sponsored ads and illuminate the significance of message explicitness and moderating factors in shaping consumer perceptions and behaviors. The findings of the study enhance our understanding of PKM in the realm of social media sponsored advertising, providing meaningful guidance to marketers and advertisers in crafting more impactful and targeted ad campaigns across Instagram and other social platforms. Ultimately, this research aids in advancing knowledge within the realm of sponsored advertising on social media and its impact on consumer behavior

Strategies for Preventing and Mitigating Counterfeit Medication From Entering the U.S. Supply Chain

Some pharmaceutical brand protection managers lack strategies to mitigate financial losses from counterfeit prescription drugs. A multilayered approach involving guiding principles, supply chain security, investigations, enforcement, advocacy, and awareness can help mitigate potential financial losses and keep patients safe. Guided by the Six Sigma define, measure, analyze, improve, and control (DMAIC) model and the fraud triangle conceptual framework, the purpose of this multiple case study was to explore strategies brand protection managers use to mitigate financial losses from counterfeit prescription drugs. Data collection included three semi-structured interviews using Zoom. Analyzing data entailed transcribing and coding themes within data and relating findings to the composite conceptual framework and peer-reviewed literature. Four key themes emerged: (a) guiding principles, (b) securing the supply chain, (c) investigations and enforcement, and (d) advocacy and awareness. The primary recommendation for pharmaceutical brand managers is to build a risk profile for each product based on knowledge of how counterfeiters behave and implement a multilayered approach for improved supply chain security while educating consumers on risks associated with purchasing medications outside the legitimate supply chain. The implications for positive social change include the possibility to inform more consumers on potential risks, which could save lives

Emergent quality issues in the supply of Chinese medicinal plants: A mixed methods investigation of their contemporary occurrence and historical persistence

Quality issues that emerged centuries ago in Chinese medicinal plants (CMP) were investigated to explore why they still persist in an era of advanced analytical testing and extensive legislation so that a solution to improve CMP quality could be proposed. This is important for 85% of the world’s population who rely on medicinal plants (MP) for primary healthcare considering the adverse events, including fatalities that arise from such quality issues. CMP are the most prevalent medicinal plants globally. This investigation used mixed-methods, including 15 interviews with CMP expert key informants (KI), together with thematic analysis that identified the main CMP quality issues, why they persisted, and informed solutions. An unexplained case example, Eleutherococcus nodiflorus (EN), was analysed by collection of 106 samples of EN, its known toxic adulterant Periploca sepium (PS), and a related substitute, Eleutherococcus senticosus (ES), across mainland China, Taiwan and the UK. Authenticity of the samples was determined using High-performance thinlayer chromatography. Misidentification, adulteration, substitution and toxicity were the main CMP quality issues identified. Adulteration was found widespread globally with 57.4% EN found authentic, and 24.6% adulterated with cardiotoxic PS, mostly at markets and traditional pharmacies. The EN study further highlighted that the reason CMP quality issues persisted was due to the laboratory-bound nature of analytical methods and testing currently used that leave gaps in detection throughout much of the supply chain. CMP quality could be more effectively tested with patented analytical technology (PAT) and simpler field-based testing including indicator strip tests. Education highlighting the long-term economic value and communal benefit of delivering better quality CMP to consumers was recommended in favour of the financial motivation for actions that lead to the persistence of well-known and recurrent CMP quality issues

A New application of Social Impact in Social Media for overcoming fake news in health

One of the challenges today is to face fake news (false information) in health due to its potential impact on people's lives. This article contributes to a new application of social impact in social media (SISM) methodology. This study focuses on the social impact of the research to identify what type of health information is false and what type of information is evidence of the social impact shared in social media. The analysis of social media includes Reddit, Facebook, and Twitter. This analysis contributes to identifying how interactions in these forms of social media depend on the type of information shared. The results indicate that messages focused on fake health information are mostly aggressive, those based on evidence of social impact are respectful and transformative, and finally, deliberation contexts promoted in social media overcome false information about health. These results contribute to advancing knowledge in overcoming fake health-related news shared in social media

Technological decision-making under scientific uncertainty : preventing mother-to-child transmission of HIV in South Africa

The normative analysis focuses on three aspects. First, it is evaluated whether the government acted correctly when it ignored expert advice that suggested the benefits of using AZT to prevent the risk of mother-to-child transmission outweighed the risks. Second, by exploring Thabo Mbeki's level of expertise, it explored whether he was in a position to make a reliable judgement about the state of the scientific discourse about the safety of AZT. Third, a proposal is made that prescribes how actors should proceed if they want to judge the authenticity of scientific controversies that are involved in the context of technological decision-making processes.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Technological decision-making under scientific uncertainty : preventing mother-to-child transmission of HIV in South Africa

The normative analysis focuses on three aspects. First, it is evaluated whether the government acted correctly when it ignored expert advice that suggested the benefits of using AZT to prevent the risk of mother-to-child transmission outweighed the risks. Second, by exploring Thabo Mbeki's level of expertise, it explored whether he was in a position to make a reliable judgement about the state of the scientific discourse about the safety of AZT. Third, a proposal is made that prescribes how actors should proceed if they want to judge the authenticity of scientific controversies that are involved in the context of technological decision-making processes.EThOS - Electronic Theses Online ServiceGBUnited Kingdo