Abortion pill marketing and sourcing on twitter following Dobbs v. Jackson supreme court ruling.

OBJECTIVE: This study examines abortion-related discourse on Twitter (X) pre-and post-Dobbs v. Jackson ruling, which eliminated the constitutional right to abortion. STUDY DESIGN: We used a custom data collection tool to collect tweets directly from Twitter using abortion-related keywords. We used the BERTopic language model and examined the top 30 retweeted and top 30 textually similar tweets from relevant topic clusters using an inductive coding approach. We also conducted statistical testing to assess potential associations between abortion themes. RESULTS: 166,799 unique tweets were collected from December 2020-December 2022. 464 unique tweets were coded for abortion-related themes with 154 identified as relevant. Of these, 66 tweets marketed abortion pills, 17 tweets were identified as offering consultations, and 91 tweets were relevant to self-managed abortion. All marketing and consultation tweets were posted post-Dobbs decision and 7 (7.69%) of self-managed tweets were posted pre-Dobbs versus 84 (92.30%) posted post-Dobbs. A positive association was found between tweets offering a medical consultation with tweets marketing abortion pills and discussing self-managed abortion. CONCLUSION: This study detected online marketing of abortion pills, consultations and discussions about self-managed abortion following the Dobbs v. Jackson ruling. These results provide more context to the type of abortion-related information that is available online.

This study examined tweets occurring both pre and post Dobbs decision and identified relevant discussions about self-managed abortion services, marketing and sale of abortion pills, and offering purported medical consultations. These findings indicate that abortion-related tweets, particularly those marketing abortion medications, increased after the Dobbs v. Jackson ruling. These findings highlight the evolving abortion information environment in the United States on Twitter, which represents a platform where health and politicised issues are commonly discussed.

Digital technologies used in clinical trial recruitment and enrollment including application to trial diversity and inclusion: A systematic review.

Background: Many clinical trials fail because of poor recruitment and enrollment which can directly impact the success of biomedical and clinical research outcomes. Options to leverage digital technology for improving clinical trial management are expansive, with potential benefits for improving access to clinical trials, encouraging trial diversity and inclusion, and potential cost-savings through enhanced efficiency. Objectives: This systematic review has two key aims: (1) identify and describe the digital technologies applied in clinical trial recruitment and enrollment and (2) evaluate evidence of these technologies addressing the recruitment and enrollment of racial and ethnic minority groups. Methods: We conducted a cross-disciplinary review of articles from PubMed, IEEE Xplore, and ACM Digital Library, published in English between January 2012 and July 2022, using MeSH terms and keywords for digital health, clinical trials, and recruitment and enrollment. Articles unrelated to technology in the recruitment/enrollment process or those discussing recruitment/enrollment without technology aspects were excluded. Results: The review returned 614 results, with 21 articles (four reviews and 17 original research articles) deemed suitable for inclusion after screening and full-text review. To address the first objective, various digital technologies were identified and characterized, which included articles with more than one technology subcategory including (a) multimedia presentations (19%, n = 4); (b) mobile applications (14%, n = 3); (c) social media platforms (29%, n = 6); (d) machine learning and computer algorithms (19%, n = 4); (e) e-consenting (24%, n = 5); (f) blockchain (5%, n = 1); (g) web-based programs (24%, n = 5); and (h) virtual messaging (24%, n = 5). Additionally, subthemes, including specific diseases or conditions addressed, privacy and regulatory concerns, cost/benefit analyses, and ethnic and minority recruitment considerations, were identified and discussed. Limited research was found to support a particular technology's effectiveness in racial and ethnic minority recruitment and enrollment. Conclusion: Results from this review illustrate that several types of technology are currently being explored and utilized in clinical trial recruitment and enrollment stages. However, evidence supporting the use of digital technologies is varied and requires further research and evaluation to identify the most valuable opportunities for encouraging diversity in clinical trial recruitment and enrollment practices.

Search Engines and Generative Artificial Intelligence Integration: Public Health Risks and Recommendations to Safeguard Consumers Online.

BACKGROUND: The online pharmacy market is growing, with legitimate online pharmacies offering advantages such as convenience and accessibility. However, this increased demand has attracted malicious actors into this space, leading to the proliferation of illegal vendors that use deceptive techniques to rank higher in search results and pose serious public health risks by dispensing substandard or falsified medicines. Search engine providers have started integrating generative artificial intelligence (AI) into search engine interfaces, which could revolutionize search by delivering more personalized results through a user-friendly experience. However, improper integration of these new technologies carries potential risks and could further exacerbate the risks posed by illicit online pharmacies by inadvertently directing users to illegal vendors. OBJECTIVE: The role of generative AI integration in reshaping search engine results, particularly related to online pharmacies, has not yet been studied. Our objective was to identify, determine the prevalence of, and characterize illegal online pharmacy recommendations within the AI-generated search results and recommendations. METHODS: We conducted a comparative assessment of AI-generated recommendations from Google's Search Generative Experience (SGE) and Microsoft Bing's Chat, focusing on popular and well-known medicines representing multiple therapeutic categories including controlled substances. Websites were individually examined to determine legitimacy, and known illegal vendors were identified by cross-referencing with the National Association of Boards of Pharmacy and LegitScript databases. RESULTS: Of the 262 websites recommended in the AI-generated search results, 47.33% (124/262) belonged to active online pharmacies, with 31.29% (82/262) leading to legitimate ones. However, 19.04% (24/126) of Bing Chat's and 13.23% (18/136) of Google SGE's recommendations directed users to illegal vendors, including for controlled substances. The proportion of illegal pharmacies varied by drug and search engine. A significant difference was observed in the distribution of illegal websites between search engines. The prevalence of links leading to illegal online pharmacies selling prescription medications was significantly higher (P=.001) in Bing Chat (21/86, 24%) compared to Google SGE (6/92, 6%). Regarding the suggestions for controlled substances, suggestions generated by Google led to a significantly higher number of rogue sellers (12/44, 27%; P=.02) compared to Bing (3/40, 7%). CONCLUSIONS: While the integration of generative AI into search engines offers promising potential, it also poses significant risks. This is the first study to shed light on the vulnerabilities within these platforms while highlighting the potential public health implications associated with their inadvertent promotion of illegal pharmacies. We found a concerning proportion of AI-generated recommendations that led to illegal online pharmacies, which could not only potentially increase their traffic but also further exacerbate existing public health risks. Rigorous oversight and proper safeguards are urgently needed in generative search to mitigate consumer risks, making sure to actively guide users to verified pharmacies and prioritize legitimate sources while excluding illegal vendors from recommendations.

Is This Safe? Examining Safety Assessments of Illicit Drug Purchasing on Social Media Using Conjoint Analysis.

Background: Illicit substance sales facilitated by social media platforms are a growing public health issue given recent increases in overdose deaths, including an alarming rise in cases of fentanyl poisoning. However, little is known about how online users evaluate what features of social media posts convey safety, which can influence their intent to source illicit substances. Objectives: This study adapts conjoint analysis which assessed how attributes of social media posts (i.e., features) influence safety evaluations of mock posts selling illicit substances. 440 participants were recruited online for self-reporting use or purchase of controlled substances or prescription medicines recreationally. The following attributes were tested: drug packaging, drug offerings, profile photo of seller, payment info provided, and use of emojis. Results: Packaging was ranked the most important attribute (Average Importance =43.68, Offering=14.94, Profile=13.86, Payment=14.11, Emoji=13.41), with posts that displayed drugs in pill bottles assessed as the most safe. Attribute levels for advertising multiple drugs, having a blank profile photo, including payment information, and including emojis also ranked higher in perceived safety. Rankings were consistent across tested demographic factors (i.e., gender, age, and income). Survey results show that online pharmacies were most likely to be perceived as safe for purchasing drugs and medications. Additionally, those who were younger in age, had higher income, and identified as female were more likely to purchase from a greater number of platforms. Conclusions: These findings can assist in developing more precise content moderation for platforms seeking to address this ongoing threat to public safety.

Rates of adverse events and related risk factors following e-cigarette use.

BACKGROUND: E-cigarettes have emerged as popular products, especially for younger populations. However, concerns regarding health effects exist and there is a notable gap in understanding the prevalence and nature of adverse events. This study aims to examine the rate of adverse events in individuals who use e-cigarettes in a large sample. METHODS: A cross-sectional survey was conducted with a sample of 4695 current and former e-cigarette users with a median age of 34 years. The survey collected data on e-cigarette use, adverse events experienced, product characteristics, related behaviors, sociodemographic factors and presence of medical comorbidities. Statistical analyses were conducted using Pearson's chi-squared tests and logistic regression. RESULTS: A total of 78.9% of respondents reported experiencing an adverse event within 6 h of using a vaping device, with the most common events being headache, anxiety and coughing. Product characteristics and related behaviors significantly influenced the risk of adverse events. There were also sociodemographic disparities, with Hispanic respondents and those with at least college-level education reporting higher rates of adverse events. CONCLUSIONS: Our study found a high rate of adverse events among e-cigarette users. We identified that certain e-cigarette product characteristics, behaviors and medical comorbidities significantly increased the risk of these events.

Identification and Characterization of Illegal Sales of Cannabis and Nicotine Delivery Products on Telegram Messaging Platform.

INTRODUCTION: Unregulated and potentially illegal sales of tobacco, nicotine, and cannabis products have been detected on various social media platforms, e-commerce sites, online retailers, and the dark web. New end-to-end encrypted messaging services are popular among online users and present opportunities for marketing, trading, and selling of these products. The purpose of this study was to identify and characterize tobacco, nicotine, and cannabis selling activity on the messaging platform Telegram. METHODS: The study was conducted in three phases: (1) identifying keywords related to tobacco, nicotine, and cannabis products for purposes of detecting Telegram groups and channel messages; (2) automated data collection from public Telegram groups; and (3) manual annotation and classification of messages engaged in marketing and selling products to consumers. RESULTS: Four keywords were identified ("Nicotine," "Vape," "Cannabis," and "Smoke") that yielded 20 Telegram groups with 262 506 active subscribers. Total volume of channel messages was 43 963 unique messages that included 3094 (7.04%) marketing/selling messages. The most commonly sold products in these groups were cannabis-derived products (83.25%, n = 2576), followed by tobacco/nicotine-derived products (6.46%, n = 200), and other illicit drugs (0.77%, n = 24). A variety of marketing tactics and a mix of seller accounts were observed, though most appeared to be individual suppliers. CONCLUSIONS: Telegram is an online messaging application that allows for custom group creation and global connectivity, but also includes unregulated activities associated with the sale of cannabis and nicotine delivery products. Greater attention is needed to conduct monitoring and enforcement on these emerging platforms for unregulated and potentially illegal cannabis and nicotine product sales direct-to-consumer. IMPLICATIONS: Based on study results, Telegram represents an emerging platform that enables a robust cannabis and nicotine-selling marketplace. As local, state, and national tobacco control regulations continue to advance sales restrictions and bans at the retail level, easily accessible and unregulated Internet-based channels must be further assessed to ensure that they do not act as conduits for exposure and access to unregulated or illegal cannabis and nicotine products.

Characterization of COVID-19 vaccine clinical trial discussions on the social question-and-answer site Quora.

INTRODUCTION: Throughout the COVID-19 pandemic, underserved populations, such as racial and ethnic minorities, were disproportionately impacted by illness, hospitalization, and death. Equity in clinical trials means that the participants in clinical trials represent the people who are most likely to have the health condition and need the treatment that the trial is testing. Infodemiology approaches examining user conversations on social media platforms have the potential to elucidate specific barriers and challenges related to clinical trial participation, including among racial and ethnic minority populations. MATERIALS AND METHODS: The study retrospectively collected and analyzed user question and answer posts from Quora in October 2021 using an inductive content coding approach. We also examined user's publicly available profile metadata to identify racial and ethnic minority users to capture their experiences, attitudes, topics, and discussions of barriers to COVID-19 vaccine trials. RESULTS: A total of 1073 questions and 7479 answers were collected. A total of 763 questions and 2548 answers were identified as related to COVID-19 vaccine clinical trials. The majority of these online interactions focused on asking questions and sharing knowledge and opinions about COVID-19 vaccine trials, including major topics related to: (a) interpreting whether clinical trial results could be trusted; (b) questions about vaccine efficacy and safety; (c) understanding trial design, regulatory considerations, and vaccine platforms; and (d) questions about trial enrollment, length, and adequate representation. Additionally, four major barriers discussed included: (i) disagreement from users regarding whether clinical trials require representation from different racial and ethnic minorities; (ii) concerns regarding the safety of trials when participating; (iii) lack of knowledge on how to participate in a trial; and (iv) questions of whether participants could withdraw from a trial to access an approved COVID-19 vaccine. CONCLUSIONS: Our study found active user discussions related to COVID-19 vaccine clinical trials on Quora, including those specific to minority health topics and those posted by a smaller group of self-identified racial and ethnic minority online users. Results from this study can help identify barriers to participation among the general public and underrepresented groups while also supporting the design of future outreach strategies to help with recruitment and inclusive trial participation.

Detection and Characterization of Web-Based Pediatric COVID-19 Vaccine Discussions and Racial and Ethnic Minority Topics: Retrospective Analysis of Twitter Data.

Background: Despite pediatric populations representing a smaller proportion of COVID-19 cases and having a less severe prognosis, those belonging to racial and ethnic minority groups are at an increased risk of developing more severe COVID-19-related outcomes. Vaccine coverage is crucial to pandemic mitigation efforts, yet since the start of the COVID-19 pandemic, vaccine hesitancy has increased and routine pediatric immunizations have decreased. Limited research exists on how vaccine hesitancy may contribute to low pediatric COVID-19 vaccine uptake among racial and ethnic minority populations. Objective: This study aimed to characterize COVID-19 vaccine-related discussion and sentiment among Twitter users, particularly among racial and ethnic minority users. Methods: We used the Twitter application programming interface to collect tweets and replies. Tweets were selected by filtering for keywords associated with COVID-19 vaccines and pediatric-related terms. From this corpus of tweets, we used the Biterm Topic Model to output topics and examined the top 200 retweeted tweets that were coded for pediatric COVID-19 vaccine relevance. Relevant tweets were analyzed using an inductive coding approach to characterize pediatric COVID-19 vaccine-related themes. Replies to relevant tweets were collected and coded. User metadata were assessed for self-reporting of race or ethnic group affiliation and verified account status. Results: A total of 863,007 tweets were collected from October 2020 to October 2021. After outputting Biterm Topic Model topics and reviewing the 200 most retweeted tweets, 208,666 tweets and 3905 replies were identified as being pediatric COVID-19 vaccine related. The majority (150,262/208,666, 72.01%) of tweets expressed vaccine-related concerns. Among tweets discussing vaccine confidence, user replies expressing agreement were significantly outweighed by those expressing disagreement (1016/3106, 32.71% vs 2090/3106, 67.29%; P<.001). The main themes identified in the Twitter interactions were conversations regarding vaccine-related concerns including adverse side effects, concerns that the vaccine is experimental or needs more testing and should not be tested on pediatric populations, the perception that the vaccine is unnecessary given the perceived low risk of pediatric infection, and conversations associated with vaccine-related confidence (ie, the vaccine is protective). Among signal tweets and replies, we identified 418 users who self-identified as a racial minority individual and 40 who self-identified as an ethnic minority individual. Among the subcodes identified in this study, the vaccine being protective was the most discussed topic by racial and ethnic minority groups (305/444, 68.7%). Conclusions: Vaccine-related concerns can have negative consequences on vaccine uptake and participation in vaccine-related clinical trials. This can impact the uptake and development of safe and effective vaccines, especially among racial and ethnic minority populations.

Detecting nuance in conspiracy discourse: Advancing methods in infodemiology and communication science with machine learning and qualitative content coding.

The spread of misinformation and conspiracies has been an ongoing issue since the early stages of the internet era, resulting in the emergence of the field of infodemiology (i.e., information epidemiology), which investigates the transmission of health-related information. Due to the high volume of online misinformation in recent years, there is a need to continue advancing methodologies in order to effectively identify narratives and themes. While machine learning models can be used to detect misinformation and conspiracies, these models are limited in their generalizability to other datasets and misinformation phenomenon, and are often unable to detect implicit meanings in text that require contextual knowledge. To rapidly detect evolving conspiracist narratives within high volume online discourse while identifying nuanced themes requiring the comprehension of subtext, this study describes a hybrid methodology that combines natural language processing (i.e., topic modeling and sentiment analysis) with qualitative content coding approaches to characterize conspiracy discourse related to 5G wireless technology and COVID-19 on Twitter (currently known as 'X'). Discourse that focused on correcting 5G conspiracies was also analyzed for comparison. Sentiment analysis shows that conspiracy-related discourse was more likely to use language that was analytic, combative, past-oriented, referenced social status, and expressed negative emotions. Corrections discourse was more likely to use words reflecting cognitive processes, prosocial relations, health-related consequences, and future-oriented language. Inductive coding characterized conspiracist narratives related to global elites, anti-vax sentiment, medical authorities, religious figures, and false correlations between technology advancements and disease outbreaks. Further, the corrections discourse did not address many of the narratives prevalent in conspiracy conversations. This paper aims to further bridge the gap between computational and qualitative methodologies by demonstrating how both approaches can be used in tandem to emphasize the positive aspects of each methodology while minimizing their respective drawbacks.

Identification and Characterization of Synthetic Nicotine Product Promotion and Sales on Instagram Using Natural Language Processing.

INTRODUCTION: There has been a rapid proliferation of synthetic nicotine products in recent years, despite newly established regulatory authority and limited research into its health risks. Previous research has implicated social media platforms as an avenue for nicotine product unregulated sales. Yet, little is known about synthetic nicotine product content on social media. We utilized natural language processing to characterize the sales of synthetic nicotine products on Instagram. METHODS: We collected Instagram posts by querying Instagram hashtags (e.g., "#tobaccofreenicotine) related to synthetic nicotine. Using BERT, collected posts were categorized into thematically related topic clusters. Posts within topic clusters relevant to study aims were then manually annotated for variables related to promotion and selling (e.g., cost discussion, contact information for offline sales). RESULTS: A total of 7,425 unique posts were collected with 2,219 posts identified as related to promotion and selling of synthetic nicotine products. Nicotine pouches (52.9%, n=1174), ENDS (30.6%, n=679), and flavored e-liquids (14.1%, n=313) were most commonly promoted. 16.1% (n=345) of posts contained embedded hyperlinks and 5.8% (n=129) provided contact information for purported offline transactions. Only 17.6% (n=391) of posts contained synthetic nicotine specific health warnings. CONCLUSIONS: In the United States, synthetic nicotine products can only be legally marketed if they have received premarket authorization from the FDA. Despite these prohibitions, Instagram appears to be a hub for potentially unregulated sales of synthetic and "tobacco-free" products. Efforts are needed by platforms and regulators to enhance content moderation and prevent unregulated online sales of existing and emerging synthetic nicotine products. IMPLICATIONS: There is limited clinical understanding of synthetic nicotine's unique health risks and how these novel products are changing over time due to regulatory oversight. Despite synthetic nicotine specific regulatory measures, such as the requirement for premarket authorization and FDA warning letters issued to unauthorized sellers, access to and promotion of synthetic nicotine is widely occurring on Instagram, a platform with over 2 billion users and one that is popular among youth and young adults. Activities include direct-to-consumer sales from questionable sources, inadequate health warning disclosure, and exposure with limited age restrictions, all conditions necessary for the sale of various tobacco products. Notably, the number of these Instagram posts increased in response to the announcement of new FDA regulations. In response, more robust online monitoring, content moderation, and proactive enforcement is needed from platforms who should work collaboratively with regulators to identify, report, and remove content in clear violation of platform policies and federal laws. Regulatory implementation and enforcement should prioritize digital platforms as conduits for unregulated access to synthetic nicotine products and other future novel and emerging tobacco products.

Tobacco Product Marketing Orders and Online Marketing and Sale of Unauthorized ENDS Products.

This study characterizes online marketing of unauthorized electronic nicotine delivery systems (ENDS).

Assessing the Impact of the Massachusetts Temporary Flavor Ban on Licensed Tobacco Retailers.

Introduction: In 2019, the state of Massachusetts signed into law the first statewide sales restrictions of flavored ENDS/tobacco products for both physical and online shops in response to a previous executive order to curb E-Cigarette, or Vaping Product, Use Associated Lung Injury (EVALI) cases that were surging throughout the nation. Methodology: This study obtained licensure data from the Massachusetts Department of Revenue, to observe the changes in retail licensure comparing the pre ban (October 2018-August 2019) and post ban periods (October 2020- August 2021). A series of linear regression tests were conducted on both periods using census tract data to explore potential associations with sociodemographic covariates, including median age, median household income, and population proportion by gender, age, and race/ethnicity groups. Results: Analysis of the Massachusetts post-ban period (October 2020-August 2021) found that new tobacco retail licenses issued decreased by 52.9% (n = 968) when compared to the pre-ban period (October 2018-August 2019) of 1831. A significant positive association was discovered between change in new retailer count and proportion male population (2.48 ± 1.05, P = .018) as well as proportion Hispanic population (1.19 ± .25, P < .001) at the census tract level. Conclusion/Discussion: Our analysis indicates that, following the temporary MA flavor sales ban, the total number of licenses decreased, though decreases were more pronounced for new licenses when compared to continuing licenses. Higher increases in new tobacco retailer density were significantly associated with concentration of male and Hispanic populations.

Tobacco/nicotine dependence as a risk factor for substance use disorders and related mental health conditions among cancer patients.

BACKGROUND: Cancer patients often face multiple comorbidities and are at risk for various mental health conditions and substance use disorders. Tobacco/nicotine dependence (TND) is a known risk factor for poor health outcomes and has been associated with psychiatric disorders including substance use disorder. However, the specific relationship between TND and the risk of substance use disorder and mental health conditions among cancer patients remains underexplored. This study aimed to assess the association between TND and the risk of comorbid conditions among cancer patients. METHODS: Data were obtained from a database of electronic health records for patients from the University of California health system. The odds for every condition among cancer patients with TND were calculated and compared with those for cancer patients without TND. ORs were adjusted for gender, ethnicity, and race. RESULTS: Three thousand seven hundred and ninety-one cancer patients with TND had 252,619 total conditions, and 51,711 cancer patients without TND had 2,310,880 conditions. After adjusting for confounders, the condition for which TND most exacerbated risk was psychoactive substance-induced organic anxiety disorder (OR = 16.3, p < 0.001). This appeared consistent with the second, third, and fifth most-exacerbated conditions: stimulant use disorder (OR = 12.8, p < 0.001), cocaine induced mental disorder (OR = 11.0, p < 0.001), and cocaine use disorder (OR = 11.0, p < 0.001). Different conditions exacerbated by TND include acute alcoholic intoxication (OR = 11.4, p < 0.001), opioid use disorder (OR = 7.6, p < 0.001), schizoaffective disorder (OR = 7.4, p < 0.001), and cannabis use disorder (OR = 6.3, p < 0.001). CONCLUSIONS: Our findings reveal a strong association between TND and an increased risk of substance use disorder and mental health conditions among cancer patients. Specifically, cancer patients with TND were at an elevated risk for psychoactive substance-induced organic anxiety disorder, stimulant use disorder, and cocaine-related disorders. Additionally, TND was associated with an increased risk of acute alcoholic intoxication, opioid use disorder, schizoaffective disorder, and cannabis use disorder. These findings underscore the need for comprehensive screening and interventions to address TND and comorbid conditions among cancer patients.

Assessing Characteristics and Compliance of Online Delta-8 Tetrahydrocannabinol Product Sellers.

Introduction: The debate over the legal status of many cannabis- and hemp-derived products, including delta-8 tetrahydrocannabinol (THC), is in question. Although low concentrations of delta-8 THC are legal at the Federal level, many states have implemented their own regulations to both allow and restrict its use and sale. Of concern, sellers with unknown legal credentials have appeared online and are actively selling this product. Materials and Methods: We characterized the marketing, sale, and compliance of online delta-8 THC sellers using (1) data collected from the Twitter Application Programming Interface with delta-8 THC-related keywords; (2) unsupervised topic modeling using the Biterm Topic Model to identify clusters of tweets involved in marketing and selling; (3) inductive coding to identify marketing and selling characteristics; and (4) web forensics and simulated shopping to determine compliance with state restrictions for delta-8 THC sales. Results: In total, 110 unique hyperlinks associated with 7085 tweets that included marketing and selling activity for delta-8 THC were collected. From these links, we conducted simulated purchasing in January 2021 to identify compliant and noncompliant websites. Among the vendors, age verification was not found in over half of websites (59, 53.63%); 60 (54.55%) did not report a physical address; and 74 (65.45%) sold delta-8 products direct-to-consumer. Sixty-seven (90.54%) of detected vendors shipped delta-8 products to addresses in states that prohibit sales. Forty-three (64.18%) of Internet Protocol addresses were located within the United States; all others were international. Conclusion: Our analysis suggests that online storefronts are illegally selling and shipping cannabinoid derivatives to U.S. consumers. Further research is needed to understand downstream health and regulatory impacts from this unregulated access.

The Influence of News Consumption Habits and Dispositional Traits on Trust in Medical Scientists.

Public trust in medical institutions is essential for ensuring compliance with medical directives. However, the politicization of public health issues and the polarized nature of major news outlets suggest that partisanship and news consumption habits can influence medical trust. This study employed a survey with 858 participants and used regression analysis to assesses how news consumption habits and information assessment traits (IATs) influence trust in medical scientists. IATs included were conscientiousness, openness, need for cognitive closure (NFCC), and cognitive reflective thinking (CRT). News sources were classified on the basis of factuality and political bias. Initially, readership of liberally biased news was positively associated with medical trust (p < 0.05). However, this association disappeared when controlling for the news source's factuality (p = 0.28), while CRT (p < 0.05) was positively associated with medical trust. When controlling for conservatively biased news sources, factuality of the news source (p < 0.05) and NFCC (p < 0.05) were positively associated with medical trust. While partisan media bias may influence medical trust, these results suggest that those who have higher abilities to assess information and who prefer more credible news sources have a greater trust in medical scientists.

Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis.

Background: Social media has emerged as a critical mass communication tool, with both health information and misinformation now spread widely on the web. Prior to the COVID-19 pandemic, some public figures promulgated anti-vaccine attitudes, which spread widely on social media platforms. Although anti-vaccine sentiment has pervaded social media throughout the COVID-19 pandemic, it is unclear to what extent interest in public figures is generating anti-vaccine discourse. Objective: We examined Twitter messages that included anti-vaccination hashtags and mentions of public figures to assess the connection between interest in these individuals and the possible spread of anti-vaccine messages. Methods: We used a data set of COVID-19-related Twitter posts collected from the public streaming application programming interface from March to October 2020 and filtered it for anti-vaccination hashtags "antivaxxing," "antivaxx," "antivaxxers," "antivax," "anti-vaxxer," "discredit," "undermine," "confidence," and "immune." Next, we applied the Biterm Topic model (BTM) to output topic clusters associated with the entire corpus. Topic clusters were manually screened by examining the top 10 posts most highly correlated in each of the 20 clusters, from which we identified 5 clusters most relevant to public figures and vaccination attitudes. We extracted all messages from these clusters and conducted inductive content analysis to characterize the discourse. Results: Our keyword search yielded 118,971 Twitter posts after duplicates were removed, and subsequently, we applied BTM to parse these data into 20 clusters. After removing retweets, we manually screened the top 10 tweets associated with each cluster (200 messages) to identify clusters associated with public figures. Extraction of these clusters yielded 768 posts for inductive analysis. Most messages were either pro-vaccination (n=329, 43%) or neutral about vaccination (n=425, 55%), with only 2% (14/768) including anti-vaccination messages. Three main themes emerged: (1) anti-vaccination accusation, in which the message accused the public figure of holding anti-vaccination beliefs; (2) using "anti-vax" as an epithet; and (3) stating or implying the negative public health impact of anti-vaccination discourse. Conclusions: Most discussions surrounding public figures in common hashtags labelled as "anti-vax" did not reflect anti-vaccination beliefs. We observed that public figures with known anti-vaccination beliefs face scorn and ridicule on Twitter. Accusing public figures of anti-vaccination attitudes is a means of insulting and discrediting the public figure rather than discrediting vaccines. The majority of posts in our sample condemned public figures expressing anti-vax beliefs by undermining their influence, insulting them, or expressing concerns over public health ramifications. This points to a complex information ecosystem, where anti-vax sentiment may not reside in common anti-vax-related keywords or hashtags, necessitating further assessment of the influence that public figures have on this discourse.

Measuring the Burden of Infodemics: Summary of the Methods and Results of the Fifth WHO Infodemic Management Conference.

Background: An infodemic is excess information, including false or misleading information, that spreads in digital and physical environments during a public health emergency. The COVID-19 pandemic has been accompanied by an unprecedented global infodemic that has led to confusion about the benefits of medical and public health interventions, with substantial impact on risk-taking and health-seeking behaviors, eroding trust in health authorities and compromising the effectiveness of public health responses and policies. Standardized measures are needed to quantify the harmful impacts of the infodemic in a systematic and methodologically robust manner, as well as harmonizing highly divergent approaches currently explored for this purpose. This can serve as a foundation for a systematic, evidence-based approach to monitoring, identifying, and mitigating future infodemic harms in emergency preparedness and prevention. Objective: In this paper, we summarize the Fifth World Health Organization (WHO) Infodemic Management Conference structure, proceedings, outcomes, and proposed actions seeking to identify the interdisciplinary approaches and frameworks needed to enable the measurement of the burden of infodemics. Methods: An iterative human-centered design (HCD) approach and concept mapping were used to facilitate focused discussions and allow for the generation of actionable outcomes and recommendations. The discussions included 86 participants representing diverse scientific disciplines and health authorities from 28 countries across all WHO regions, along with observers from civil society and global public health-implementing partners. A thematic map capturing the concepts matching the key contributing factors to the public health burden of infodemics was used throughout the conference to frame and contextualize discussions. Five key areas for immediate action were identified. Results: The 5 key areas for the development of metrics to assess the burden of infodemics and associated interventions included (1) developing standardized definitions and ensuring the adoption thereof; (2) improving the map of concepts influencing the burden of infodemics; (3) conducting a review of evidence, tools, and data sources; (4) setting up a technical working group; and (5) addressing immediate priorities for postpandemic recovery and resilience building. The summary report consolidated group input toward a common vocabulary with standardized terms, concepts, study designs, measures, and tools to estimate the burden of infodemics and the effectiveness of infodemic management interventions. Conclusions: Standardizing measurement is the basis for documenting the burden of infodemics on health systems and population health during emergencies. Investment is needed into the development of practical, affordable, evidence-based, and systematic methods that are legally and ethically balanced for monitoring infodemics; generating diagnostics, infodemic insights, and recommendations; and developing interventions, action-oriented guidance, policies, support options, mechanisms, and tools for infodemic managers and emergency program managers.