Smoking is a leading cause of serious disease among smokers, with the greatest risks arising from the combustion of tobacco and inhalation of smoke. Tobacco Harm Reduction (THR) encompasses strategies aimed at lowering the health risks associated with smoking, while potentially involving continued use of nicotine or tobacco products. Next Generation Products (NGPs), such as Electronic Vapor Products (EVPs), deliver nicotine without combustion and may play a role in THR. To predict the long-term population impact of NGPs, mathematical approaches such as Dynamic Population Models (DPM) have been widely used. These models typically rely on longitudinal data to simulate individual transitions between smoking and NGP use over time. However, such longitudinal data are often difficult to obtain. To address this limitation, we propose a novel modeling approach based on prevalence data. Historical prevalence trends were modeled using time-series forecasting methods and combined with relative risk estimates to project mortality under two scenarios: a cigarette-only market and a market with both cigarettes and EVPs. Validation against US data and comparison with a reference transition-based model demonstrated both accuracy and computational efficiency. When applied to the United Kingdom, the model predicts that EVP availability could prevent between 360,000 and 850,000 premature deaths by 2040, with up to 1.53 million deaths averted under higher switching assumptions. The largest reductions are observed in lung cancer, cardiovascular disease, and stroke mortality. These findings suggest that EVPs could substantially reduce smoking-related mortality. Furthermore, the proposed time-series DPM provides a practical and scalable framework for assessing harm reduction, particularly in settings where longitudinal data are limited.
(i)Reported metal levels in disposable devices can be strongly influenced by pre-testing storage and handling;(ii)Comparisons across products should prioritize mass yields and exposure scenarios rather than concentration;(iii)Risk characterization should report sensitivity to realistic use duration and intermittent exposure.
Exploring the filtration characteristics of smoke aerosol particles in cellulose acetate filters and developing a prediction model are vital for designing cigarettes with low “tar” and harmful components. This study employed solanesol as a marker for smoke aerosol particles to investigate its filtration performance in cellulose acetate filters. The effects of length, pressure drop, tow specifications, and flow velocity on the filtration of smoke aerosol particles by cellulose acetate filters were analyzed using an HPLC-DAD method. The results indicated that the filtration of smoke aerosol particles by cellulose acetate filters conformed to the first-order kinetic filtration equation. For filters with the same circumference, the filtration coefficient of smoke aerosol particles was linearly proportional to the pressure drop per unit filter length. Moreover, the tow specifications had no significant effect on the filtration of smoke aerosol particles. The filtration coefficient of smoke aerosol particles decreased with increasing flow velocity. Based on these characteristics, a prediction model for the filtration of smoke aerosol particles by cellulose acetate filters was established, incorporating flow velocity, pressure drop per unit filter length, and circumference. This model was employed to predict the filtration coefficients of smoke aerosol particles in different filters, achieving prediction deviations of less than 5%. Applying the model to predict the filtration efficiencies of “tar” in different filters, the prediction deviations were below 15%. The model can accurately predict the filtration efficiencies of particulate matter in cellulose acetate filters, which provides technical support for cigarette harm reduction, and also offers valuable references for aerosol particles filtration in the atmospheric and industrial fields.
Cigarette smoking is causally associated with a number of chronic diseases in which inflammation plays an important pathogenic role including pulmonary carcinoma, chronic bronchitis, emphysema, atherosclerosis, and thrombosis. In addition, cigarette smoking can exacerbate Crohn's disease and rheumatoid arthritis. In both the particulate and vapor phases, cigarette smoke contains a number of chemicals with proinflammatory or cytotoxic potential. In contrast with cigarette smoking, nicotine has been shown to ameliorate several inflammatory conditions via activation of α7- and α9-nicotinic cholinergic receptors. The chemistry of electronic cigarette (e-cigarette) vaping aerosols is comprised mainly of glycerol, propylene glycol, nicotine, and flavors. The nicotine can be found in either the free base or salt forms depending on the particular product design. E-cigarettes containing nicotine salts have the potential for nicotine exposure levels higher than cigarettes as the sensory cues associated with tobacco smoke nicotine (containing a mixture of free base nicotine and nicotine salts) have been dampened. In this review and analysis, the role of nicotine in inducing inflammation in the absence of other main-stream cigarette smoke constituents is examined. Established and putative mechanisms by which nicotine influences inflammatory pathways in users of nicotine-containing products are described toward better understanding nicotine's potential contribution to the risk profile. For the majority of diseases with an inflammatory component induced by cigarette smoking, evidence suggests that nicotine is not the causative agent. E-cigarette aerosols should limit the diversity of chemical constituents and minimize exposure to proinflammatory chemicals, e.g., acrolein, acetaldehyde, formaldehyde, and heavy metals.
The Population Assessment of Tobacco and Health (PATH) is a commonly-used longitudinal survey on nicotine/tobacco product use meant to represent the US population. Accurate surveillance to prevent youth electronic nicotine delivery systems (ENDS) use requires understanding the impact of methodological differences in Wave 6 (∼2021), specifically older ages (14–17 vs. usual 12–17) and survey mode (some telephone interviews vs. usual self-completed).
