Prevalence of ENDS and JUUL Use, by Smoking Status, in National Samples of Young Adults and Older Adults in the U.S

Does vaping affect the taste and smell perception? An exploratory study with New Zealand young adults

OBJECTIVE

The study aimed to assess potential effects of vaping on individual taste and smell perception in a sample of young adult New Zealanders.

DESIGN

This cross-sectional study measured taste and smell perception using intensity and hedonic ratings to two olfactory (i.e., vanillin, methional) and two gustatory stimuli (i.e., sucrose, monosodium glutamate), representing sweet and savoury flavours. Detection sensitivities to sucrose and vanillin were also assessed using a forced choice detection paradigm aligned with the signal detection framework. MANCOVAs were employed to compare sensory perception between groups based on vaping use frequency. Additional regression analyses were conducted to identify potential predictors of intensity and hedonic sensory ratings.

SETTING

Participants were recruited from the University of Otago student population and surrounding neighbourhoods of Dunedin, New Zealand in 2023.

PARTICIPANTS

The study included 213 university students (98 vapers and 115 non-vapers) RESULTS: We found a significant difference in hedonic ratings for vanillin, indicating a stronger preference among non-vapers. However, no other differences between the two groups were significant. Notably, the use of tobacco and mint flavours were emerged as significant predictors for hedonic responses to the savoury smell and sweet taste stimulus, respectively. No significant differences were observed between groups in the ability to detect weak stimuli.

CONCLUSIONS

Our findings suggest that vape use, particularly with specific flavours, may be associated with alterations in hedonic responses to smells. This finding may have potential implications for how vaping affects on food preferences and dietary choices.

An 8-year longitudinal study of long-term, continuous users of electronic cigarettes

BACKGROUND AND AIMS

E-cigarettes have been available for over 15 years, but relatively little is known about long-term users. Our aim was to describe change over time in behaviours, attitudes and dependence in long-term users.

DESIGN AND PARTICIPANTS

A longitudinal study of 375 e-cigarette users enrolled on the Internet in 2012-2016 and surveyed again in 2021 (8 years later on average), who continuously used e-cigarettes in the interval.

FINDINGS

Fewer people in 2021 (11 %) than at baseline (33 %) had smoked tobacco in the past 31 days. Participants switched from second-generation models at baseline (e.g. Ego) to box mods in 2021 (e.g. iStick), they used larger refill bottles, they used home-made e-liquids twice as often, they used tobacco flavours less often and the nicotine concentration in e-liquids decreased from 12 to 6 mg/mL. There was no change over time in the time to the first e-cigarette puff of the day, but an e-cigarette dependence score of 0-100 decreased from 75 to 60, the frequency and strength of urges to vape decreased (from 31 % to 18 % of "strong" urges) and the proportion of people who said they would be likely to succeed if they tried to stop vaping increased. Compared to baseline, fewer people in 2021 reported vaping to cope with cravings or other smoking cessation symptoms, and fewer people reported vaping to quit smoking, to avoid relapse into smoking, or to reduce their tobacco consumption (p < 0.001 for all differences).

CONCLUSIONS

In long-term, continuous users, over a period of 8 years, substantial changes were observed in the models of e-cigarettes used, in the flavours and strength of e-liquids, and in the reasons for vaping. Their level of nicotine dependence tended to decrease over time. These users were satisfied with e-cigarettes and vaped mostly because they felt that vaping was less dangerous than smoking, and for enjoyment.

Associations of daily alcohol, cannabis, combustible tobacco, and e-cigarette use with same-day co-use and poly-use of the other substances

BACKGROUND

Young adults frequently use alcohol, cannabis, and tobacco together. Given the increased prevalence of e-cigarette use and recreational cannabis use, we investigated daily patterns of alcohol, cannabis, and tobacco use and distinguished combustible tobacco from e-cigarettes.

METHODS

Young adult college students (N=341) reporting past-month alcohol and cannabis use "at the same time so that their effects overlapped" completed two 28-day bursts of repeated daily surveys. Exposures were day- and person-level use of each substance. Outcomes were (1) same-day co-use of each remaining substance or (2) poly-use of the other substances.

RESULTS

Daily use of alcohol, cannabis, combustible cigarettes, and e-cigarettes increased the odds of same-day co-use of the other substances (except combustible tobacco with e-cigarettes) and each poly-use outcome. The influence of person-level substance use on daily substance use was less consistent. Only e-cigarette use increased the odds of daily alcohol use. Use of either tobacco product but not alcohol increased the odds of daily cannabis use. Person-level alcohol and cannabis use increased the odds of daily use of either tobacco product but use of one tobacco product was not associated with daily use of the other product.

CONCLUSIONS

These findings increase our understanding of emerging daily patterns of alcohol, cannabis, and tobacco co-use, and the impact of different tobacco products. Future work is needed to extend this research into non-college samples and people who use tobacco but do not use alcohol and cannabis simultaneously, and examine daily chronologies of multiple substances that could serve as dynamic markers of risk.

Electronic cigarette use and risk of COVID-19 among young adults without a history of cigarette smoking

It is unknown whether use of e-cigarettes increases susceptibility to COVID-19. In a large clinical sample of young adults, we evaluated whether current or ever e-cigarette use was associated with polymerase chain reaction (PCR)-confirmed COVID-19. To address the confounding of combustible smoking, the sample was restricted to never smokers. This retrospective cohort study analyzed data from the electronic health records of 74,853 young adults (aged 18-35 years), without a history of cigarette smoking, who were screened for e-cigarette use (current, former, never) in the Kaiser Permanente Northern California (KPNC) healthcare system from 3/5/2020 (baseline) to 11/30/2020 (pre-vaccine). COVID-19 risk was estimated in time-to-event analyses using multivariable Cox proportional hazard regression models, adjusted for socio-demographics and medical comorbidities. E-cigarette status in the cohort was: 1.6% current, 1.2% former, and 97.2% never. During follow-up, 1965 (2.6%) patients acquired COVID-19. We did not find evidence that current (vs never) e-cigarette use was associated with risk of COVID-19 (aHR = 1.12 95%CI:0.77-1.62). However, we did find suggestive evidence that former (versus never) e-cigarette use may be associated with greater risk of COVID-19 (aHR = 1.39 95%CI:0.98-1.96). While e-cigarette use is associated with health risks for young adults, results from this study suggest that current use of e-cigarettes may not increase susceptibility for COVID-19 among young adults who have never smoked cigarettes.

Characterizing Participant Perceptions about Smoking-Cessation Pharmacotherapy and E-Cigarettes from Facebook Smoking-Cessation Support Groups

The prevalence of smoking among young adults aged 19-28 years old in the United States persists at rates of 14.3%. Young adults underutilize pharmacotherapy for smoking cessation, and the use of e-cigarettes has increased. We analyzed comments from online smoking-cessation support groups to understand young-adult smokers' views of pharmacotherapy and e-cigarettes, to provide a more in-depth insight into the underutilization of pharmacotherapy. A qualitative analysis was performed on comments about pharmacotherapy and e-cigarettes from participants enrolled in online smoking-cessation support groups in 2016-2020. A codebook was developed with a deductive approach to code the comments, followed by thematic analysis. Eighteen themes were identified, with four dominant themes: interest, benefit, knowledge, and flavor. Participants expressed less interest in both nicotine-replacement therapy and e-cigarettes; moreover, they expressed unfamiliarity with and misconceptions about pharmacotherapy, and recognized the enticing flavors of e-cigarettes. Participants often felt e-cigarettes were not useful for smoking cessation, but the flavors of e-cigarettes were appealing for use. Participants had mixed opinions about the use of e-cigarettes for smoking cessation, but predominantly felt e-cigarettes were not useful for smoking cessation. The use of social media may be an effective way to address misconceptions about pharmacotherapy for smoking cessation and increase willingness to accept assistance.

A Mixed-Methods Study on Use of Different Tobacco Products among Younger and Older Adults with Lower and Higher Levels of Nicotine Exposure in California in 2019-2020

An increasing number of tobacco products are entering the market, offering a variety of options to attain desired nicotine intake. However, little is known about the effect of this diverse marketplace on the experiences and nicotine exposure among tobacco users. A mixed-methods study examined experiences with tobacco products among individuals with relatively lower or higher levels of biomarkers of nicotine exposure. Semi-structured interviews were conducted with younger and older adults to examine tobacco use behaviors, addiction to tobacco products, and nicotine effects. Younger and older adults provided similar narratives about addiction and nicotine effects, which were similar across age groups, product types (i.e., ENDS, combustible cigarettes, or oral tobacco products), and having lower or higher nicotine exposure. Some individuals with higher nicotine exposure who switched from combustible cigarettes to another product (e.g., ENDS) found similar or greater access and perceived addiction to nicotine. Adults with higher and lower levels of nicotine exposure provided narratives consistent with features of nicotine addiction, regardless of age and products used. Availability of multiple tobacco products may be associated with greater access and exposure to nicotine. Addiction may occur at low levels of use and with non-cigarette products.

Identifying Electronic Nicotine Delivery System Brands and Flavors on Instagram: Natural Language Processing Analysis

Background

Electronic nicotine delivery system (ENDS) brands, such as JUUL, used social media as a key component of their marketing strategy, which led to massive sales growth from 2015 to 2018. During this time, ENDS use rapidly increased among youths and young adults, with flavored products being particularly popular among these groups.

Objective

The aim of our study is to develop a named entity recognition (NER) model to identify potential emerging vaping brands and flavors from Instagram post text. NER is a natural language processing task for identifying specific types of words (entities) in text based on the characteristics of the entity and surrounding words.

Methods

NER models were trained on a labeled data set of 2272 Instagram posts coded for ENDS brands and flavors. We compared three types of NER models—conditional random fields, a residual convolutional neural network, and a fine-tuned distilled bidirectional encoder representations from transformers (FTDB) network—to identify brands and flavors in Instagram posts with key model outcomes of precision, recall, and F1 scores. We used data from Nielsen scanner sales and Wikipedia to create benchmark dictionaries to determine whether brands from established ENDS brand and flavor lists were mentioned in the Instagram posts in our sample. To prevent overfitting, we performed 5-fold cross-validation and reported the mean and SD of the model validation metrics across the folds.

Results

For brands, the residual convolutional neural network exhibited the highest mean precision (0.797, SD 0.084), and the FTDB exhibited the highest mean recall (0.869, SD 0.103). For flavors, the FTDB exhibited both the highest mean precision (0.860, SD 0.055) and recall (0.801, SD 0.091). All NER models outperformed the benchmark brand and flavor dictionary look-ups on mean precision, recall, and F1. Comparing between the benchmark brand lists, the larger Wikipedia list outperformed the Nielsen list in both precision and recall.

Conclusions

Our findings suggest that NER models correctly identified ENDS brands and flavors in Instagram posts at rates competitive with, or better than, others in the published literature. Brands identified during manual annotation showed little overlap with those in Nielsen scanner data, suggesting that NER models may capture emerging brands with limited sales and distribution. NER models address the challenges of manual brand identification and can be used to support future infodemiology and infoveillance studies. Brands identified on social media should be cross-validated with Nielsen and other data sources to differentiate emerging brands that have become established from those with limited sales and distribution.

1,2-Propylene Glycol: A Biomarker of Exposure Specific to e-Cigarette Consumption

Over the past decade, new emerging tobacco and nicotine-delivery products have changed the tobacco landscape. Especially, electronic cigarettes (ECs) have been suggested to be considered for tobacco harm reduction, reinforcing the need to identify novel biomarkers of exposure (BoE) specific to the EC use as this would complement exposure assessment and product compliance monitoring. Therefore, a sensitive LC-MS/MS method for the quantification of 1,2-propylene glycol (PG) and glycerol (G), the main e-liquid constituents, was established. PG and G were analyzed in plasma and urine samples from a clinical study comparing five nicotine product user groups, users of combustible cigarettes (CC), electronic cigarettes (EC), heated tobacco products (HTP), oral tobacco (OT), and oral/dermal nicotine delivery products (used for nicotine replacement therapy, NRT) with a control group of non-users (NU). Data demonstrate significantly elevated PG levels in urine and plasma in EC users compared to users of CC, HTP, NRT, OT as well as NU. In addition, PG in plasma and urine of vapers significantly correlated with nicotine (plasma) and total nicotine equivalents (urine), biomarkers reflecting product consumption, emphasizing the high specificity of PG as a BoE for EC consumption. We therefore suggest the use of PG as BoE in urine and/or plasma in order to monitor EC use compliance in exposure assessments.