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.

A geospatial analysis of age disparities in resolute localities of tobacco and vaping-specific storefronts in California.

INTRODUCTION: Concomitant with the popularization of vaping, vape shops have dramatically proliferated over the past years. This study assesses whether vape storefronts in California are significantly associated with density of different age groups, and whether this differs between tobacco storefronts or non-specific tobacco retailers. METHODS: Addresses for licensed tobacco retailers were obtained from the California Department of Tax and Fee Administration. Business names and addresses were used to obtain store categories cross-referenced from Yelp. Using a cross-sectional ecological design, stores categorized as 'Vape Shop' or 'Tobacco Shop' were geolocated and compared with age-related variables from the American Community Survey. Regression was conducted in R to determine relationships between age group concentration, in ventiles, and proportion of tracts with tobacco-specific or vape-specific stores. Geospatial visualization was conducted using ArcGIS. RESULTS: We found 848 vape shops, 820 tobacco shops, 419 categorized as both, and 20320 retailers with neither category. Overall, 1800 tobacco and/or vape shops were categorized in 1557 of California's 23194 census tracts. A positive linear association was found between ventiles of two age categories, 20-24 and 25-34 years, and proportion of tracts with vape-specific or tobacco-specific shops separately. CONCLUSIONS: Positive associations were found for ages 20-34 years but not for other ages, suggesting vape shops are strategically located in areas populated by young adults. Location-based targeting increases access, thereby increasing proportion of tobacco users, and could be a critical factor in e-cigarette uptake and use. Further study to identify additional age-related demographic characteristics among clientele of tobacco storefronts is warranted.

Characterizing Self-Reported Tobacco, Vaping, and Marijuana-Related Tweets Geolocated for California College Campuses.

Introduction: College-aged youth are active on social media yet smoking-related social media engagement in these populations has not been thoroughly investigated. We sought to conduct an exploratory infoveillance study focused on geolocated data to characterize smoking-related tweets originating from California 4-year colleges on Twitter. Methods: Tweets from 2015 to 2019 with geospatial coordinates in CA college campuses containing smoking-related keywords were collected from the Twitter API stream and manually annotated for discussions about smoking product type, sentiment, and behavior. Results: Out of all tweets detected with smoking-related behavior, 46.7% related to tobacco use, 50.0% to marijuana, and 7.3% to vaping. Of these tweets, 46.1% reported first-person use or second-hand observation of smoking behavior. Out of 962 tweets with user sentiment, the majority (67.6%) were positive, ranging from 55.0% for California State University, Long Beach to 95.8% for California State University, Los Angeles. Discussion: We detected reporting of first- and second-hand smoking behavior on CA college campuses representing possible violation of campus smoking bans. The majority of tweets expressed positive sentiment about smoking behaviors, though there was appreciable variability between college campuses. This suggests that anti-smoking outreach should be tailored to the unique student populations of these college communities. Conclusion: Among tweets about smoking from California colleges, high levels of positive sentiment suggest that the campus climate may be less receptive to anti-smoking messages or adherence to campus smoking bans. Further research should investigate the degree to which this varies by campuses over time and following implementation of bans including validating using other sources of data.