State Cannabis and Psychedelic Legislation and Microdosing Interest in the US.

Importance: Despite growing interest in psychedelics, there is a lack of routine population-based surveillance of psychedelic microdosing (taking "subperceptual" doses of psychedelics, approximately one-twentieth to one-fifth of a full dose, over prolonged periods). Analyzing Google search queries can provide insights into public interest and help address this gap. Objective: To analyze trends in public interest in microdosing in the US through Google search queries and assess their association with cannabis and psychedelic legislative reforms. Design, Setting, and Participants: In this cross-sectional study, a dynamic event-time difference-in-difference time series analysis was used to assess the impact of cannabis and psychedelic legislation on microdosing search rates from January 1, 2010, to December 31, 2023. Google search rates mentioning "microdosing," "micro dosing," "microdose," or "micro dose" within the US and across US states were measured in aggregate. Exposure: Enactment of (1) local psychedelic decriminalization laws; (2) legalization of psychedelic-assisted therapy and statewide psychedelic decriminalization; (3) statewide medical cannabis use laws; (4) statewide recreational cannabis use laws; and (5) all cannabis and psychedelic use restricted. Main Outcome and Measures: Microdosing searches per 10 million Google queries were measured, examining annual and monthly changes in search rates across the US, including frequency and nature of related searches. Results: Searches for microdosing in the US remained stable until 2014, then increased annually thereafter, with a cumulative increase by a factor of 13.4 from 2015 to 2023 (7.9 per 10 million to 105.6 per 10 million searches, respectively). In 2023, there were 3.0 million microdosing searches in the US. Analysis at the state level revealed that local psychedelic decriminalization laws were associated with an increase in search rates by 22.4 per 10 million (95% CI, 7.5-37.2), statewide psychedelic therapeutic legalization and decriminalization were associated with an increase in search rates by 28.9 per 10 million (95% CI, 16.5-41.2), statewide recreational cannabis laws were associated with an increase in search rates by 40.9 per 10 million (95% CI, 28.6-53.3), and statewide medical cannabis laws were associated with an increase in search rates by 11.5 per 10 million (95% CI, 6.0-16.9). From August through December 2023, 27.0% of the variation in monthly microdosing search rates between states was explained by differences in cannabis and psychedelics legal status. Conclusion and Relevance: This cross-sectional study found that state-led legislative reforms on cannabis and psychedelics were associated with increased public interest in microdosing psychedelics.

A Digital Cohort Approach for Social Media Monitoring: A Cohort Study of People Who Vape E-Cigarettes.

INTRODUCTION: The evidence hierarchy in public health emphasizes longitudinal studies, whereas social media monitoring relies on aggregate analyses. Authors propose integrating longitudinal analyses into social media monitoring by creating a digital cohort of individual account holders, as demonstrated by a case study analysis of people who vape. METHODS: All English language X posts mentioning vape or vaping were collected from January 1, 2017 through December 31, 2020. The digital cohort was composed of people who self-reported vaping and posted at least 10 times about vaping during the study period to determine the (1) prevalence, (2) success rate, and (3) timing of cessation behaviors. RESULTS: There were 25,112 instances where an account shared at least 10 posts about vaping, with 619 (95% CI=616, 622) mean person-days and 43,810,531 cumulative person-days of observation. Among a random sample of accounts, 39% (95% CI=35, 43) belonged to persons who vaped. Among this digital cohort, 27% (95% CI=21, 33) reported making a quit attempt. For all first quit attempts, 26% (95% CI=19, 33) were successful on the basis of their subsequent vaping posts. Among those with a failed first cessation attempt, 13% (95% CI=6, 19) subsequently made an additional quit attempt, of whom 36% (95% CI=11, 61) were successful. On average, a quit attempt occurred 531 days (95% CI=474, 588) after their first vaping-related post. If their quit attempt failed, any second quit attempt occurred 361 days (95% CI=250, 474) after their first quit attempt. CONCLUSIONS: By aligning with standard epidemiologic surveillance practices, this approach can greatly enhance the usefulness of social media monitoring in informing public health decision making, such as yielding insights into the timing of cessation behaviors among people who vape.

Using Large Language Models to Support Content Analysis: A Case Study of ChatGPT for Adverse Event Detection.

This study explores the potential of using large language models to assist content analysis by conducting a case study to identify adverse events (AEs) in social media posts. The case study compares ChatGPT's performance with human annotators' in detecting AEs associated with delta-8-tetrahydrocannabinol, a cannabis-derived product. Using the identical instructions given to human annotators, ChatGPT closely approximated human results, with a high degree of agreement noted: 94.4% (9436/10,000) for any AE detection (Fleiss κ=0.95) and 99.3% (9931/10,000) for serious AEs (κ=0.96). These findings suggest that ChatGPT has the potential to replicate human annotation accurately and efficiently. The study recognizes possible limitations, including concerns about the generalizability due to ChatGPT's training data, and prompts further research with different models, data sources, and content analysis tasks. The study highlights the promise of large language models for enhancing the efficiency of biomedical research.

Characteristics of X (Formerly Twitter) Community Notes Addressing COVID-19 Vaccine Misinformation.

This study evaluated the topics, accuracy, and credibility of X (formerly Twitter) Community Notes addressing COVID-19 vaccination.

Correction: Twitter and Facebook posts about COVID-19 are less likely to spread misinformation compared to other health topics.

[This corrects the article DOI: 10.1371/journal.pone.0261768.].

Regulate Artificial Intelligence in Health Care by Prioritizing Patient Outcomes.

This Viewpoint argues for a shift in focus by the White House executive order on artificial intelligence from regulatory targets to patient outcomes.

Managing HIV During the COVID-19 Pandemic: A Study of Help-Seeking Behaviors on a Social Media Forum.

Although numerous editorials claim the COVID-19 pandemic has disproportionately impacted vulnerable populations, particularly those affected by HIV, these claims have received limited empirical evaluation. We analyzed posts to Reddit's r/HIVAIDS from January 3, 2012 through April 30, 2022 to (a) assess changes in the volume of posts during the pandemic and (b) determine the needs of HIV affected communities. There were cumulatively 100% (95%CI: 75-126) more posts than expected since the US declared a pandemic emergency. The most prevalent themes in these posts were for obtaining an HIV + diagnosis (representing 34% (95%CI:29-40) of all posts), seeking HIV treatment (20%; 95%CI:16-25), finding psychosocial support (16%; 95%CI:12-20), and tracking disease progression (8%; 95%CI:5-11). Discussions about PrEP and PEP were the least common, representing less than 6% of all posts each. Social media has increasingly become an important health resource for vulnerable populations seeking information, advice, and support. Public health organizations should recognize how the lay public uses social media and collaborate with social media companies to ensure that the needs of help-seekers on these platforms are met.

#MedEd: Medical Education and Knowledge Translation on Social Media.

This Viewpoint discusses the potential educational benefits of social media in the health sciences.

Evaluating Artificial Intelligence Responses to Public Health Questions.

This cross-sectional study analyzes the quality of ChatGPT responses to public health questions.

Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum.

Importance: The rapid expansion of virtual health care has caused a surge in patient messages concomitant with more work and burnout among health care professionals. Artificial intelligence (AI) assistants could potentially aid in creating answers to patient questions by drafting responses that could be reviewed by clinicians. Objective: To evaluate the ability of an AI chatbot assistant (ChatGPT), released in November 2022, to provide quality and empathetic responses to patient questions. Design, Setting, and Participants: In this cross-sectional study, a public and nonidentifiable database of questions from a public social media forum (Reddit's r/AskDocs) was used to randomly draw 195 exchanges from October 2022 where a verified physician responded to a public question. Chatbot responses were generated by entering the original question into a fresh session (without prior questions having been asked in the session) on December 22 and 23, 2022. The original question along with anonymized and randomly ordered physician and chatbot responses were evaluated in triplicate by a team of licensed health care professionals. Evaluators chose "which response was better" and judged both "the quality of information provided" (very poor, poor, acceptable, good, or very good) and "the empathy or bedside manner provided" (not empathetic, slightly empathetic, moderately empathetic, empathetic, and very empathetic). Mean outcomes were ordered on a 1 to 5 scale and compared between chatbot and physicians. Results: Of the 195 questions and responses, evaluators preferred chatbot responses to physician responses in 78.6% (95% CI, 75.0%-81.8%) of the 585 evaluations. Mean (IQR) physician responses were significantly shorter than chatbot responses (52 [17-62] words vs 211 [168-245] words; t = 25.4; P < .001). Chatbot responses were rated of significantly higher quality than physician responses (t = 13.3; P < .001). The proportion of responses rated as good or very good quality (≥ 4), for instance, was higher for chatbot than physicians (chatbot: 78.5%, 95% CI, 72.3%-84.1%; physicians: 22.1%, 95% CI, 16.4%-28.2%;). This amounted to 3.6 times higher prevalence of good or very good quality responses for the chatbot. Chatbot responses were also rated significantly more empathetic than physician responses (t = 18.9; P < .001). The proportion of responses rated empathetic or very empathetic (≥4) was higher for chatbot than for physicians (physicians: 4.6%, 95% CI, 2.1%-7.7%; chatbot: 45.1%, 95% CI, 38.5%-51.8%; physicians: 4.6%, 95% CI, 2.1%-7.7%). This amounted to 9.8 times higher prevalence of empathetic or very empathetic responses for the chatbot. Conclusions: In this cross-sectional study, a chatbot generated quality and empathetic responses to patient questions posed in an online forum. Further exploration of this technology is warranted in clinical settings, such as using chatbot to draft responses that physicians could then edit. Randomized trials could assess further if using AI assistants might improve responses, lower clinician burnout, and improve patient outcomes.

Online searches for SGLT-2 inhibitors and GLP-1 receptor agonists correlate with prescription rates in the United States: An infodemiological study.

Several clinical trials have demonstrated that many SGLT-2 inhibitors (SGLT2i) and GLP-1 receptor agonists (GLP-1 RA) can reduce the risk of cardiovascular events in patients with Type 2 diabetes and atherosclerotic cardiovascular disease. Recent reports indicate an underutilization of new cardiometabolic drugs, including SGLT2i and GLP-1 RA. We aimed to evaluate the use of online search volumes to reflect United States prescription rates. A repeated cross-sectional analysis of Google search volumes and corresponding data from the IQVIA National Prescription Audit (NPA) of pharmacy dispensing of newly prescribed drugs was performed. Monthly data for online searches and prescription between January 1, 2016 and December 31, 2021 were collected for selected SGLT2i and GLP-1 RA. Prescription data for drugs classes (SGLT2i and GLP-1 RA) and individual drugs were calculated as the total of queried data for branded drug names. Trends were analyzed for visual and quantitative correlation as well as predictive patterns. Overall, online searches increased by 157.6% (95% CI: 142.2-173.1%) and 295.2% (95% CI: 257.7-332.6%) for SGLT2i and GLP-1RA between 2016 and 2021. Prescription rates raised by 114.6% (95% CI: 110.8-118.4%) and 221.0% (95% CI: 212.1-229.9%) for SGLT2i and GLP-1RA for this period. Correlation coefficients (range 0.86-0.99) were strongest for drugs with growing number of prescriptions, for example dapagliflozin, empagliflozin, ertugliflozin, dulaglutide, and semaglutide. Online searches might represent an additional tool to monitor the utilization trends of cardiometabolic drugs. Associations were strongest for drugs with reported cardioprotective effect. Thus, trends in online searches complement conventionally acquired data to reflect and forecast prescription trends of cardiometabolic drugs.

Association of Online Search Trends With Vaccination in the United States: June 2020 Through May 2021.

Stagnating COVID-19 vaccination rates and vaccine hesitancy remain a threat to public health. Improved strategies for real-time tracking and estimation of population-level behavior regarding vaccinations are needed. The aim of this study was to evaluate whether online search trends for COIVD-19 and influenza mirror vaccination rates. State-level weekly fraction of online searches for top vaccination-related search terms and CDC vaccination data were obtained from June 1, 2020, to May 31, 2021. Next, trends in online search and vaccination data for COVID-19 and influenza were analyzed for visual and quantitative correlation patterns using Spearman's rank correlation analysis. Online searches in the US for COVID-19 vaccinations increased 2.71-fold (95% CI: 1.98-3.45) in the 4 weeks after the FDA emergency authorization compared to the precedent 4 weeks. In March-April 2021, US online searches reached a plateau that was followed by a decline of 83.3% (95% CI: 31.2%-135.3%) until May 31, 2021. The timing of peaks in online searches varied across US states. Online searches were strongly correlated with vaccination rates (r=0.71, 95% CI: 0.45 - 0.87), preceding actual reported vaccination rates in 44 of 51 states. Online search trends preceded vaccination trends by a median of 3.0 weeks (95% CI: 2.0-4.0 weeks) across all states. For influenza vaccination searches, seasonal peaks in September-October between 2016-2020 were noted. Influenza search trends highly correlated with the timing of actual vaccinations for the 2019-2020 (r=0.82, 95% CI: 0.64 - 0.93) and 2020-2021 season (r=0.91, 95% CI: 0.78 - 0.97). Search trends and real-world vaccination rates are highly correlated. Temporal alignment and correlation levels were higher for influenza vaccinations; however, only online searches for COVID-19 vaccination preceded vaccination trends. These findings indicate that US online search data can potentially guide public health efforts, including policy changes and identifying geographical areas to expand vaccination campaigns.

Twitter and Facebook posts about COVID-19 are less likely to spread misinformation compared to other health topics.

The COVID-19 pandemic brought widespread attention to an "infodemic" of potential health misinformation. This claim has not been assessed based on evidence. We evaluated if health misinformation became more common during the pandemic. We gathered about 325 million posts sharing URLs from Twitter and Facebook during the beginning of the pandemic (March 8-May 1, 2020) compared to the same period in 2019. We relied on source credibility as an accepted proxy for misinformation across this database. Human annotators also coded a subsample of 3000 posts with URLs for misinformation. Posts about COVID-19 were 0.37 times as likely to link to "not credible" sources and 1.13 times more likely to link to "more credible" sources than prior to the pandemic. Posts linking to "not credible" sources were 3.67 times more likely to include misinformation compared to posts from "more credible" sources. Thus, during the earliest stages of the pandemic, when claims of an infodemic emerged, social media contained proportionally less misinformation than expected based on the prior year. Our results suggest that widespread health misinformation is not unique to COVID-19. Rather, it is a systemic feature of online health communication that can adversely impact public health behaviors and must therefore be addressed.

Characterizing Self-Reports of Self-Identified Patient Experiences with Methadone Maintenance Treatment on an Online Community during COVID-19.

Background: The coronavirus disease (COVID-19) pandemic has impacted patients receiving methadone maintenance treatment (MMT) through opioid treatment programs (OTPs), especially because of the unique challenges of the care delivery model. Previously, documentation of patient experiences during emergencies often comes years after the fact, in part because there is a substantial data void in real-time. Methods: We extracted 308 posts that mention COVID-19 keywords on r/methadone, an online community for patients receiving MMT to share information, on Reddit occurring between January 31, 2020 and September 30, 2020. 215 of these posts self-report an impact to their MMT. Using qualitative content analysis, we characterized the impacts described in these posts and identified four emergent themes describing patients' experience of impacts to MMT during COVID-19. Results: The themes included (1) 54.4% of posts reporting impediments to accessing their methadone, (2) 28.4% reporting impediments to accessing physicial OTPs, (3) 19.5% reporting having to self-manage their care, and (4) 4.7% reporting impediments to accessing OTP providers and staff. Conclusions: Patients described unanticipated consequences to one-size-fits-all policies that are unevenly applied resulting in suboptimal dosing, increased perceived risk of acquiring COVID-19 at OTPs, and reduced interaction with OTP providers and staff. While preliminary, these results are formative for follow-up surveillance metrics for patients of OTPs as well as digitally-mediated resource needs for this online community. This study serves as a model of how social media can be employed during and after emergencies to hear the lived experiences of patients for informed emergency preparedness and response.

Monitoring HIV testing and pre-exposure prophylaxis information seeking by combining digital and traditional data.

BACKGROUND: Public health is increasingly turning to non-traditional digital data to inform HIV prevention and control strategies. We demonstrate a parsimonious method using both traditional survey and internet search histories to provide new insights into HIV testing and pre-exposure prophylaxis (PrEP) information seeking that can be easily extended to other settings. METHOD: We modeled how US internet search volumes from 2019 for HIV testing and PrEP compared against expected search volumes for HIV testing and PrEP using state HIV prevalence and socioeconomic characteristics as predictors. States with search volumes outside the upper and lower bound confidence interval were labeled as either over or under performing. State performance was evaluated by (a) Centers for Disease Control and Prevention designation as a hotspot for new HIV diagnoses (b) expanding Medicaid coverage. RESULTS: Ten states over-performed in models assessing information seeking for HIV testing, while eleven states under-performed. Thirteen states over-performed in models assessing internet searches for PrEP information, while thirteen states under-performed. States that expanded Medicaid coverage were more likely to over perform in PrEP models than states that did not expand Medicaid coverage. While states that were hotspots for new HIV diagnoses were more likely to over perform on HIV testing searches. CONCLUSION: Our study derived a method of measuring HIV and PrEP information seeking that is comparable across states. Several states exhibited information seeking for PrEP and HIV testing that deviated from model assessments. Statewide search volume for PrEP information was affected by a state's decision to expand Medicaid coverage. Our research provides health officials with an innovative way to monitor statewide interest in PrEP and HIV testing using a metric for information-seeking that is comparable across states.