The stability in adolescents’ e-cigarette preferred type or brand also has not been examined. E-cigarette brands that are popular today among adolescents can deliver nicotine from a single compact pod that equals that of a pack of cigarettes, in attractive flavors, and with easy concealment for use in settings where cigarettes may be forbidden . These characteristics may facilitate the progression from intermittent to frequent use and nicotine dependence. Alternatively, low nicotine content and/or low device appeal may result in adolescents losing interest in e-cigarettes over time, with diminishing frequency and dependence risk. Among adults, research indicates that evolving from a simpler e-cigarette device to a more complex modifiable device is a common pattern and is associated with greater dependence on e-cigarettes. Despite rapid growth in the e-cigarette market in recent years, research has not yet examined whether or how adolescents’ preferred devices change over time, particularly with regard to nicotine delivery and exposure. Finally, minimal research has examined changes in adolescents’ reasons for initiating, continuing, and/or quitting e-cigarette use over time. In cross-sectional survey studies, adolescents’ top reasons for experimenting with e-cigarettes include curiosity, appealing flavors, friends’ use, and perceived benefits compared with cigarettes. However, reasons may shift over time, as adolescents move from experimentation to sustained use. The literature on youth initiation and transition to regular use of combustible cigarettes shows that media/marketing and social influences motivate initiation,hydroponic table whereas the drive for nicotine due to addiction motivates regular use. These nicotine product use patterns observed with combustible cigarettes warrant investigation with e-cigarettes.
With e-cigarettes, adolescents who begin experimenting because of curiosity or appealing flavors may subsequently use to alleviate withdrawal symptoms. The present study followed a cohort of adolescent e-cigarette users over 12 months’ time to examine patterns of e-cigarette use frequency, nicotine exposure, and dependence, product use and flavor preference, and motivators to use and cease use. The primary objectives were to determine persistence in e-cigarette and dual use and the stability in frequency and dependence measures of e-cigarette use. We also examined changes in device and e-liquid preferences and reasons for using e-cigarettes. This longitudinal study adds to the literature by providing an understanding of shifts in tobacco and nicotine product use over time among adolescents based on self-report and biomarkers of exposure.Adolescents from the San Francisco Bay Area who reported having used an e-cigarette at least once in the past 30 days and at least 10 times in their lives were recruited for a longitudinal study on teen vaping between May 2015 and April 2017. Advertisements were posted on social media and in the community around the Bay Area. Interested individuals were directed to the study Web site, where they could submit their information to be contacted by study staff to complete eligibility screening. Eligible participants who provided informed consent were scheduled for a baseline session where they completed self-report measures and provided a saliva sample for cotinine testing. Participants returned for follow-up measures and cotinine testing 6 and 12 months after baseline. Study incentives were $30 for the baseline, $35 for the 6-month, and $40 for the 12-month follow-up visits. Parental consent was waived under California law 6929. Cessation information and local treatment options were provided. The research design and study procedures were approved by the University of California, San Francisco Institutional Review Board.In this longitudinal study of adolescent e-cigarette use with self-reported and biomarker data, 80.3% of the sample continued to use e-cigarettes at 12 months, with significantly greater ecigarette use frequency, dependence, and nicotine exposure. The percentage of daily e-cigarette users doubled from 14.5% at baseline to 29.8% at 12-month follow-up.
The patterns of ecigarette use observed over time indicate substantial persistence and the use of greater amounts of nicotine over time . These findings lend support to concerns regarding the addictiveness of e-cigarettes for adolescents. In the United States, prevalence of past-month e-cigarette use increased dramatically among adolescents in 2018, whereas cigarette use declined and cannabis use remained constant. Results of this study suggest that increased prevalence of recent e-cigarette use may lead to frequent use, dependence, and greater nicotine exposure. Dependence scores at baseline were low on average, with most participants meeting a classification of “not dependent,” and 13.3% meeting a classification of moderate to heavy dependence. By 12 months, the percentage classified with moderate to heavy dependence increased to 23.3%. These findings would suggest that factors other than dependence are driving early use of e-cigarettes, and that over the course of just 1 year, more teens become daily users and more heavily dependent. Along with the self-reported increase in frequency of e-cigarette use and dependence, cotinine levels increased over time, reflecting increased exposure to nicotine. The increase in cotinine levels may be both the result of increased dependence and a catalyst for the development of dependence. Adolescents who become increasingly dependent on e-cigarettes may increase their nicotine use, thereby worsening dependence. Notably, devices with higher nicotine yield became increasingly popular over the course of the 12-month trial, consistent with the reports of greater nicotine dependence and higher cotinine levels. Transitions from single to dual and dual to single nicotine product use were observed in approximately one in three users over the study period. None of the baseline dual users abstained from both products at either follow-up, which may be partially due to their higher dependence on e-cigarettes at baseline, as well as the normalization of smoking behavior and associations between smoking cues that can perpetuate use of both products. Consistent with prior research, adolescent participants offered a wide range of reasons for e-cigarette use. The top three reasons for initiating and continuing use were socializing, enjoyment, and flavors. The top three reasons for quitting were a desire for self-improvement, difficulty maintaining an e-cigarette device, and getting in trouble for vaping at home or school. The top flavors were fruit, menthol/mint, and candy.
Taken together with experimental research demonstrating the influence of flavors on adolescents’ product choices, these findings suggest that efforts to reduce adolescent e-cigarette use ought to include regulatory action that addresses kid-friendly flavors. Little research has examined adolescents’ reasons for quitting e-cigarette use, and our findings preliminarily suggest that adolescents perceive parental controls and appropriate disciplinary consequences to be impactful.In 2018 in the United States, over 650 bicyclists died, and there were almost 158,000 bicycle-automobile collision–related injuries.1 Current bicycle injury prevention measures that have been proven include bicycle helmet programs and bicycle helmet laws.Promising prevention measures include active lighting, increased rider visibility, and roadway modifications.Trauma registries can be used to identify modifiable injury risk factors for trauma prevention efforts, including bicyclist collisions with automobiles. However, these may miss factors useful for prevention of bicycle-automobile collisions, such as vehicle speeds, driver intoxication, and patient group characteristics, such as financial stress, educational level, and languages spoken. Geographic information systems use software that can relate seemingly unrelated data to provide better understanding of spatial patterns and relationships. The GIS studies in the trauma literature include optimizing trauma center location,identifying under serviced areas for quality trauma care,hot spot and cluster analysis for traffic collisions,and helicopter basing and efficacy.Trauma registries typically already contain some geospatial data, such as home and injury location addresses. Traffic records databases contain details about bicyclist automobile collisions not typically found in trauma registries, such as vehicle speeds, driver intoxication,grow rack street and lighting conditions, and driver or cyclist fault.These records also include accurate exact collision locations, allowing GIS mapping. The GIS analysis also allows the use of census tract demographic data for both the collision location and patient’s home for analysis.We hypothesize that GIS analysis of trauma registry data matched with a traffic records database could identify additional risk factors for bicycle-automobile injury helmet use or intoxication. We also hypothesize that the addition of GIS analysis to the trauma registry will better inform injury prevention efforts.The trauma registry of the UC San Diego Level I trauma center was used retrospectively to identify bicycle-motor vehicle collision admissions from January 1, 2010, to December 31, 2018. Data collected included demographics, home and injury location addresses, injury severity scores, blood alcohol, toxicology, helmet use, hospital length of stay and mortality. Matching of the registry cases with the California Statewide Integrated Traffic Records System was done to provide collision, bicyclist, driver, and geospatial information for bicycle-automobile collisions within the County of San Diego for the same period. Statewide Integrated Traffic Records System is administered by the California Highway Patrol, and includes all traffic collisions in California with a law enforcement report. Matching was deterministic and was done by bicyclist age, bicyclist sex, zip code, date and time ±1 hour between the SWITRS collision time and the trauma registry admission time transfers from outside the County of San Diego were excluded. Outcomes of interest included toxicology—available as “Alcohol Involved” in SWITRS collision data and “Party Sobriety” under SWITRS party data or from blood alcohol and urine toxicology in the trauma registry. Helmet use was found as reported in SWITRS or the trauma registry. Missing variables in either database were managed by excluding such cases from analysis using that variable. Geocoding, mapping, and geospatial analysis of matched case SWITRS collision locations was done using ArcGIS Pro 2.6 .
Registry home addresses of cases were also geocoded and used with US Census Bureau census tract data to provide the below poverty level percentage for home census tracts. The educational attainment level for cases was done by selecting the predominant education level within the patient’s home address census tract from the US Census Bureau’s American Community Survey 2014 to 2018 5-year estimates, Table B15002. The language spoken at home was selected by home address census tract in the U.S. Census Bureau’s American Community Survey 2014 to 2018 5-year estimates, Table B16007. Locations of bike lanes in San Diego County for analysis of their associations with collisions were obtained from SanGIS for Bike Paths , which are physically separated from traffic; bike lanes , which are defined by pavement striping and signage on streets; and Bike Routes , which are shared use with motor vehicle traffic in the same travel lane and designated by signs only. Locations of alcoholic beverage control licenses in San Diego County for analysis of association with intoxicated bicyclist collisions was obtained from the California Department of Alcoholic Beverage Control via SanGIS and esri. The spatial clustering of bicycle-automobile collisions and hot spots were assessed using spatial auto correlation via the Getis-Ord Gi* statistic in ArcGIS Pro.Analyses of descriptive and tabular data were performed with IBM SPSS Statistics 27. χ2 Analysis was done for categorical data, means were compared via ANOVA and distributions of educational level, LOS, and head-neck Abbreviated Injury Scale were compared with the Mann-Whitney U test. Two-sided p values less than 0.05 were considered significant. Binary logistic regression was used for the outcome variables of helmet use and toxicology, selecting factors with a p value less than 0.01 in the univariate analysis. The SWITRS data were used under the terms of the data use agreement provided by the California Highway Patrol. The study was exempted from further review by the UC San Diego Human Research Protections Program.We have shown that GIS analysis of trauma registry data matched with a traffic accident records database can identify additional risk factors for bicycle-automobile injuries. We have also shown that our injury prevention efforts will be better informed by the hotspot analysis which clearly demonstrates clusters in specific geographic areas of the catchment area. The ability to add census tract data to registry data shows associations of education level and poverty level on bicycle helmet use. Census tract data also provides useful information for injury prevention efforts such as the predominant language spoken at home in target areas. Trauma registries, whether trauma center-based or nationally collected, should be constructed to allow geospatial analysis. Helmet use by bicyclists has been shown to reduce the risk of serious injury.Despite this, we saw relatively low use of helmets in admitted bicyclists.This may be reflected in our results showing adverse census tract poverty level and educational levels having an association with lack of helmet use. Unhelmeted cyclists in this study were also less likely to be discharged to rehabilitation or long-term care facilities, this is likely due to their being relatively underinsured compared with helmeted cyclists.