Key Risk Factors Associated With Electronic Nicotine Delivery Systems Use Among Adolescents

Harnessing machine learning in contemporary tobacco research

Machine learning (ML) has the potential to transform tobacco research and address the urgent public health crisis posed by tobacco use. Despite the well-documented health risks, cessation rates remain low. ML techniques offer innovative solutions by analyzing vast datasets to uncover patterns in smoking behavior, genetic predispositions, and effective cessation strategies. ML can predict smoking-induced non-communicable diseases (SiNCDs) like lung cancer and postmenopausal osteoporosis by identifying biomarkers and genetic profiles, generating personalized predictions, and guiding interventions. It also improves prediction of infant tobacco smoke exposure, distinguishes secondhand and thirdhand smoke, and enhances protection strategies for children. Data-driven, personalized approaches using ML track real-time data for personalized feedback and offer timely interventions, continuously improving cessation strategies. Overall, ML provides sophisticated predictive models, enhances understanding of complex biological mechanisms, and enables personalized interventions, demonstrating significant potential in the fight against the tobacco epidemic.

Adolescent-specific risk and protective factors of substance use among high school students in the United States: A cross-sectional study

Objective: Substance use among U.S. adolescents is a critical public health concern. This study evaluates risk and protective factors for substance use among high school students. Methods: Using data from the 2022 National Survey on Drug Use and Health, a cross-sectional analysis of 6,072 high school students was conducted. Multivariable logistic regression analyzed associations between substance use (alcohol, tobacco, illicit drugs) and adolescent-specific factors, adjusting for sex, grade, health insurance, and poverty status. Results: Overall, 35.6% of students reported using alcohol, tobacco, or illicit drugs in the past year. Antisocial behavior significantly increased the odds of alcohol (AOR = 2.54, 95% CI = 2.02-3.19), tobacco (AOR = 2.36, 95% CI = 1.92-2.89), and illicit drug use (AOR = 2.66, 95% CI = 2.22-3.19; p < .001). Religious involvement reduced the odds of alcohol (AOR = 0.79, 95% CI = 0.64-0.97, p = .026), tobacco (AOR = 0.71, 95% CI = 0.56-0.90, p = .006), and illicit drug use (AOR = 0.59, 95% CI = 0.45-0.77, p < .001). Conclusions: One in three high school students reported substance use. Antisocial behaviors increase risk, while religious involvement offers protection. Interventions targeting these factors may reduce adolescent substance use.

EAP and ECPCP urge ban on novel nicotine- (NNCPS) and non-nicotine-containing products (NNDS) to youth

AIM

We want to verify the correlation between the increasing use of novel nicotine-containing products (NNCPs) and non-nicotine delivery products (NNDPs) among young individuals and the escalating negative health consequences, necessitating their prohibition.

METHODS

We performed a comprehensive analysis of the most relevant literature about the utilisation of NNCPs and NNDPs among young individuals and their health effects.

RESULTS

Despite being initially seen as less harmful alternatives, for smokers aiming to quit, these products have become more popular due to misleading marketing claims. Teenagers using NNCPs and NNDPs, despite having no smoking history, are more likely to transition to tobacco smoking. Consistent use can lead to health issues like pulmonary damage, asthma, and cardiovascular and ocular problems.

CONCLUSION

The EAP and the ECPCP endorse the WHO's appeal to outlaw these hazardous products. They urge European governments to forbid the sale of NNCPs and NNDPs to children and adolescents in order to safeguard their well-being. They also propose specific recommendations (box 4) to support this cause.

Controlling smoking: A smoking epidemic model with different smoking degrees in deterministic and stochastic environments

Engaging in smoking not only leads to substantial health risks but also imposes considerable financial burdens. To deepen our understanding of the mechanisms behind smoking transmission and to address the tobacco epidemic, we examined a five-dimensional smoking epidemic model that accounts for different degrees of smoking under both deterministic and stochastic conditions. In the deterministic case, we determine the basic reproduction number, analyze the stability of equilibria with and without smoking, and investigate the existence of saddle-node bifurcation. Our analysis reveals that the basic reproduction number cannot completely determine the existence of smoking, and the model possesses bistability, indicating its dynamic is susceptible to interference from environmental noises. In the stochastic case, we establish sufficient conditions for the ergodic stationary distribution and the elimination of smokers by constructing appropriate Lyapunov functions. Numerical simulations suggest that the effects of inevitable random fluctuations in the natural environment on controlling the smoking epidemic may be beneficial, harmful, or negligible, which are closely related to the noise intensities, initial smoking population sizes, and the effective exposure rate of smoking transmission (β). Given the uncontrollable nature of environmental random effects, effective smoking control strategies can be achieved by: (1) accurate monitoring of initial smoking population sizes, and (2) implementing effective measures to reduce β. Therefore, it is both effective and feasible to implement a complete set of strong MPOWER measures to control smoking prevalence.