Biomarkers of nicotine exposure correlate with the Hooked on Nicotine Checklist among adolescents in California, United States

Biomarkers of Waterpipe Tobacco Smoke Exposure: A Systematic Review and Meta-Analysis

INTRODUCTION

The prevalence of waterpipe tobacco smoking is increasing globally. Biomarkers of waterpipe tobacco smoke (WTS) exposure are less studied.

AIMS AND METHODS

To identify the types of biomarkers of WTS exposure and estimate changes in biomarker concentrations pre- to post-WTS exposure. PubMed, Embase, Web of Science, CINAHL Plus, PsycINFO, and Cochrane Library were searched for studies up to April 24, 2023. The types of biomarkers were identified. Random-effects models were used to estimate changes in biomarker concentrations pre- to post-WTS exposure.

RESULTS

Seventy-three studies involving 3755 participants exposed to WTS (49% male, mean age: 24.8 years) and 11 types of biomarkers of WTS exposure were identified. The biomarkers included tobacco alkaloids, expired carbon monoxide (eCO), carboxyhemoglobin (COHb), tobacco-specific nitrosamines, volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs), heavy metals, unmetabolized VOCs, unmetabolized PAHs, furan metabolites, and heterocyclic aromatic amines. Compared with pre-WTS exposure, eCO (breath; mean difference [MD] 27.00 ppm; 95% confidence interval [CI]: 20.91 to 33.08), COHb (blood; MD 4.30%; 95%CI: 2.57 to 6.03), COHb (breath; MD 7.14%; 95%CI: 4.96 to 9.31), nicotine (blood; MD 8.23 ng/mL; 95%CI: 6.27 to 10.19), and cotinine (urine; MD 110.40 ng/mL; 95%CI: 46.26 to 174.54) significantly increased post-WTS exposure.

CONCLUSIONS

Biomarkers of WTS exposure were systematically identified. The similarity between the biomarkers of WTS exposure and those of cigarette smoke and higher concentrations of some biomarkers post-WTS exposure underscore the need for further research on applying biomarkers in surveillance, interventions, and regulations to mitigate the harms of waterpipe tobacco smoking.

IMPLICATIONS

This study provides the first comprehensive overview of biomarkers investigated and available for assessing WTS exposure and their concentration changes in the human body. Researchers can use biomarkers such as eCO, COHb, nicotine, and cotinine to measure the health risks associated with WTS exposure and objectively evaluate the effectiveness of public health interventions aimed at reducing waterpipe tobacco smoking. Public health policymaking can also be informed through increased biomarker concentrations following WTS exposure, to implement regulations and public health education campaigns on limiting or preventing waterpipe tobacco smoking.

Adaptation and Validation of the Hooked on Nicotine Checklist (HONC) with Spanish Adolescents

INTRODUCTION

The expansion of new tobacco consumption formats and electronic vapor products threatens to reverse the trend of declining smoking rates that had been observed among younger people in recent decades. Early detection in the health sector requires screening tools that have been adapted and validated in our context. This study aims to translate, culturally adapt, pilot and empirically validate the Hooked on Nicotine Checklist (HONC) with Spanish adolescents.

METHODS

The process of translation and cultural adaptation included the following stages: direct translation, synthesis of translations, back translation, consolidation with a committee of experts and pre-test, along with a cognitive interview, which served as pilot testing. Empirical validation was conducted with a sample of 1027 adolescents aged between 12 and 18 (M=15.40; SD=1.638).

RESULTS

The results obtained confirm that the HONC is a brief, clear and easy-to-understand tool, with appropriate psychometric properties. A Cronbach's alpha value of 0.90 was obtained. Sensitivity, specificity, PPV and NPV indices reached values of 0.56, 0.94, 0.79 and 0.83, respectively. Cut-off point 1 is the one reaching the best balance between the two values. CFA showed a model good overall fit.

CONCLUSION

This study makes the Spanish version of the HONC available to researchers and clinicians, so that it can be used with sufficient psychometric guarantees.

E-cigarette and combustible cigarette cessation patterns, reasons, and methods among adolescents, young adults, and adults

Research is limited regarding adolescents' and young adults' (AYA) patterns, methods of, and reasons for cigarette or e-cigarette cessation. Further, while adults may try to use e-cigarettes to quit combustible cigarettes, little is known about how adults then quit e-cigarettes. This study utilizes a national, cross-sectional online survey of 6131 diverse participants aged 13-40 years to examine reasons for quitting e-cigarettes or cigarettes, quit methods, and quit attempt outcomes among AYAs and adults. In our sample, 3137 (51.2%) had ever used an e-cigarette, of whom 2310 (37.7%) were aged 13-24 years and 827 (13.5%) were 25-40 years old; 2387 (38.9%) had ever used a combustible cigarette (1440 [23.5%] were 13-24 years old and 947 [15.4%] were 25-40 years old). Among e-cigarette ever-users, 39.4% of 13-24-year-olds intended to quit in the next 6 months, and 36.9% had a serious plan for quitting in the next 30 days; 25.2% wanted to decrease the amount they used while 34.8% wanted to quit completely. Similar rates were found among e-cigarette ever users aged 25-40 years, as well as past 30-day e-cigarette users, cigarette ever-users, and past 30-day cigarette users across all ages. "Cold turkey" (41.0%) followed by "tried to cut down slowly by vaping/smoking less often or fewer puffs" (25.5%) was the most common quit method among e-cigarette ever-users and cigarette ever-users of all ages. Further study of effective tobacco cessation methods to help both AYAs and adults successfully quit nicotine, whether from cigarettes or e-cigarettes, is urgently needed.

Nicotine dependence among young adults: Comparing exclusive ENDS users to exclusive cigarette smokers

PURPOSE

No studies of young adults have compared symptoms of nicotine dependence among exclusive past 30-day (P30D) ENDS users versus exclusive P30D cigarette smokers.

METHODS

Participants at Wave 14 (Fall 2021) of The Texas Adolescent and Tobacco Marketing Surveillance System (TATAMS) (n = 2,341; mean age = 20.95 years old) who reported P30D exclusive ENDS use (n = 212) and P30D exclusive cigarette smoking (n = 46). Symptoms of nicotine dependence were measured with the Hooked On Nicotine Checklist (the 10-item HONC scale). An independent samples t-test compared average HONC scores between groups, and a Pearson (or Fisher's Exact) Chi-Square (X2) test compared the prevalence of symptom(s) between groups.

RESULTS

The average HONC score was significantly higher for exclusive P30D ENDS users than exclusive P30D cigarette smokers (3.51 vs. 1.91, p ≤ 0.001). Compared to exclusive P30D cigarette smokers, a significantly higher proportion of exclusive P30D ENDS users reported having felt addicted (45.28% vs. 15.22%, p ≤ 0.001), having strong cravings (50.00% vs. 28.26%, p = 0.007), difficulty not using in prohibited places (i.e., school or work) (24.06% vs. 4.35%, p = 0.002), difficulty concentrating (21.23% vs. 6.52%, p = 0.021), and feeling nervous, restless, or anxious (28.77% vs. 10.87%, p = 0.012).

CONCLUSIONS

Symptoms of nicotine dependence were elevated among young adults who were exclusive P30D users of ENDS relative to exclusive P30D cigarette smokers.

Nicotine Dependence from Different E-Cigarette Devices and Combustible Cigarettes among US Adolescent and Young Adult Users

E-cigarettes, the most popular tobacco product among adolescents, vary widely in design and nicotine composition; thus, different devices may have different addictive potential. However, few studies examine levels of nicotine dependence across devices among adolescent and young adult (AYA) e-cigarette users. To assess the extent of nicotine dependence among US AYA (ages 13-24) by e-cigarette device type, we conducted a large, national, cross-sectional survey (n = 4351) and used the Hooked on Nicotine Checklist (HONC) to assess levels of nicotine dependence among those who had used disposable, pod-based, and/or mods/other e-cigarette devices in the past 30 days. We also examined HONC scores among those who had used combustible cigarettes in the past 30 days, whether with or without using e-cigarettes. Patterns of nicotine dependence were comparable across those who had used a combustible cigarette and/or e-cigarette in the past 30 days, with 91.4% of combustible cigarette users, 80.7% of disposable e-cigarette users, 83.1% of pod-based e-cigarette users, and 82.5% of mods/other e-cigarette users showing signs of nicotine dependence, as measured by endorsing at least one HONC symptom. This pattern persisted when analyses were restricted to e-cigarette only users, with more than 70% of all e-cigarette only past-30-day users endorsing at least one HONC symptom, across all types of devices. A thorough understanding of the extent and presentation of nicotine dependence among AYA will help researchers, public health officials, and clinicians recognize and manage AYA nicotine dependence.