E-commerce licensing loopholes: a case study of online shopping for tobacco products following a statewide sales restriction on flavoured tobacco in California.

INTRODUCTION: Retailer licensing programmes can be an effective method of enforcing tobacco control laws, but most programmes do not require e-commerce retailers to obtain licenses. California's implementation of a statewide flavour restriction (Senate Bill 793 (SB-793)) in December 2022 enforced through its tobacco retailer licensing programme presented an opportunity to assess whether the exclusion of e-commerce in the definition of 'tobacco retailer' might have resulted in a shift in consumer behaviour towards e-commerce. METHODS: To examine the association between SB-793 implementation and online shopping for tobacco, we collected weekly Google search rates related to online shopping for cigarettes and vaping products in California from January 2018 to May 2023. We compared observed rates of shopping queries after SB-793 implementation to counterfactual expected rates and prediction intervals (PI) calculated from autoregressive iterative moving average models fit to historical trends. Content analysis was performed on the search results to identify websites marketing flavoured vaping products and menthol cigarettes. RESULTS: The week SB-793 was implemented, shopping queries were 194.4% (95% PI 100.8% to 451.5%) and 161.7% (95% PI 81.7% to 367.5%) higher than expected for cigarettes and vapes, respectively. Cigarette shopping queries remained elevated significantly for 11 weeks and vape shopping queries for 6 weeks. All search results contained links to websites that offered flavoured vaping products or menthol cigarettes to Californian consumers. DISCUSSION: These findings raise concerns about potential loopholes in policy enforcement created by the absence of explicit regulations on e-commerce sales in retailer licensing programmes. Strengthening regulations to include e-commerce and monitoring e-commerce compliance are recommended to enhance the impact of laws enforced through retailer licensing programmes.

Quantifying Public Interest in Police Reforms by Mining Internet Search Data Following George Floyd's Death.

BACKGROUND: The death of George Floyd while in police custody has resurfaced serious questions about police conduct that result in the deaths of unarmed persons. OBJECTIVE: Data-driven strategies that identify and prioritize the public's needs may engender a public health response to improve policing. We assessed how internet searches indicative of interest in police reform changed after Mr Floyd's death. METHODS: We monitored daily Google searches (per 10 million total searches) that included the terms "police" and "reform(s)" (eg, "reform the police," "best police reforms," etc) originating from the United States between January 1, 2010, through July 5, 2020. We also monitored searches containing the term "police" with "training," "union(s)," "militarization," or "immunity" as markers of interest in the corresponding reform topics. RESULTS: The 41 days following Mr Floyd's death corresponded with the greatest number of police "reform(s)" searches ever recorded, with 1,350,000 total searches nationally. Searches increased significantly in all 50 states and Washington DC. By reform topic, nationally there were 1,220,000 total searches for "police" and "union(s)"; 820,000 for "training"; 360,000 for "immunity"; and 72,000 for "militarization." In terms of searches for all policy topics by state, 33 states searched the most for "training," 16 for "union(s)," and 2 for "immunity." States typically in the southeast had fewer queries related to any police reform topic than other states. States that had a greater percentage of votes for President Donald Trump during the 2016 election searched more often for police "union(s)" while states favoring Secretary Hillary Clinton searched more for police "training." CONCLUSIONS: The United States is at a historical juncture, with record interest in topics related to police reform with variability in search terms across states. Policy makers can respond to searches by considering the policies their constituencies are searching for online, notably police training and unions. Public health leaders can respond by engaging in the subject of policing and advocating for evidence-based policy reforms.

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.

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.

Evaluating Artificial Intelligence Responses to Public Health Questions.

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