Modeling the Population Health Impact of ENDS in the U.S

The "Gateway" hypothesis: evaluation of evidence and alternative explanations

BACKGROUND

Electronic nicotine delivery systems (ENDS) offer a substantial harm reduction opportunity for adults who smoke and are unlikely to quit. However, a major concern about ENDS is their use by non-smoking youth, and particularly whether ENDS are acting as a "gateway" that leads youth to later start smoking cigarettes. However, evidence for the gateway hypothesis can be interpreted in alternative ways, e.g. that youth who have certain characteristics were already predisposed to use both ENDS and cigarettes ("common liability" explanation).

AIMS

This commentary provides an evaluation of the gateway hypothesis that is accessible by a lay audience. This paper first reviews and evaluates the evidence interpreted as supporting the gateway hypothesis. Important alternative explanations (i.e., common liability) are discussed, as are different types of evidence (i.e., population-level trends) that can help differentiate between these competing explanations.

OVERVIEW

Evidence for the gateway hypothesis is based on the finding that youth who use ENDS are more likely to also smoke cigarettes. However, this evidence suffers from an important flaw: these studies fail to fully account for some youths' pre-existing tendency to use products containing nicotine, and inappropriately interpret the results as ENDS use causing some youth to smoke. Common liability studies suggest that ENDS use does not, in and of itself, directly cause youth to later smoke cigarettes, beyond their pre-existing tendency to use products containing nicotine. Population-level trends show that youth cigarette smoking declined faster after ENDS use became common, which contradicts the central prediction of the gateway hypothesis (i.e. that youth smoking would be more common following ENDS uptake, than otherwise be expected).

CONCLUSION

Evidence offered in support of the gateway hypothesis does not establish that ENDS use causes youth to also smoke cigarettes. Instead, this evidence is better interpreted as resulting from a common liability to use both ENDS and cigarettes. Population-level trends are inconsistent with the gateway hypothesis, and instead are consistent with (but do not prove) ENDS displacing cigarettes. Policies based on misinterpreting a causal gateway effect may be ineffective at best, and risk the negative unintended consequence of increased cigarette smoking.

Reply to Commentary on "Adult Smokers' Complete Switching Away from Cigarettes at 6, 9, and 12 Months After Initially Purchasing a JUUL e-Cigarette" and the Adult JUUL Switching and Smoking Trajectories (ADJUSST) Study

BACKGROUND

This is authors' reply to the Commentary on our publication entitled "Adult smokers' Complete Switching Away from Cigarettes at 6, 9, and 12 Months after Initially Purchasing a JUUL e-Cigarette."

METHODS AND RESULTS

Analyses addressed questions about follow-up rates and missed responses in the Adult JUUL Switching and Smoking Trajectories (ADJUSST) Study. Results demonstrate limited potential for selection bias, as participants who missed surveys were similar to those with complete data, and re-contact of participants who missed a follow-up indicated almost half were not smoking. Imputing smoking behavior for missing data would likely introduce bias and is not appropriate. The study demonstrated that JUUL products can facilitate high rates of complete switching away from cigarettes as suggested in previous experimental and observational studies. The ADJUSST cohort, including baseline nonsmokers, demonstrates a net reduction in smoking prevalence. Moreover, population modeling considering both benefits and harms demonstrated a net population benefit.

CONCLUSION

While the ADJUSST Study is not without limitations, the findings are consistent with multiple streams of real-world evidence that indicate that ENDS can facilitate switching among adults who smoke, and provide population benefits.

From cause and effect to causes and effects

It is now-at least loosely-acknowledged that most health and clinical outcomes are influenced by different interacting causes. Surprisingly, medical research studies are nearly universally designed to study-usually in a binary way-the effect of a single cause. Recent experiences during the coronavirus disease 2019 pandemic brought to the forefront that most of our challenges in medicine and healthcare deal with systemic, that is, interdependent and interconnected problems. Understanding these problems defy simplistic dichotomous research methodologies. These insights demand a shift in our thinking from 'cause and effect' to 'causes and effects' since this transcends the classical way of Cartesian reductionist thinking. We require a shift to a 'causes and effects' frame so we can choose the research methodology that reflects the relationships between variables of interest-one-to-one, one-to-many, many-to-one or many-to-many. One-to-one (or cause and effect) relationships are amenable to the traditional randomized control trial design, while all others require systemic designs to understand 'causes and effects'. Researchers urgently need to re-evaluate their science models and embrace research designs that allow an exploration of the clinically obvious multiple 'causes and effects' on health and disease. Clinical examples highlight the application of various systemic research methodologies and demonstrate how 'causes and effects' explain the heterogeneity of clinical outcomes. This shift in scientific thinking will allow us to find the necessary personalized or precise clinical interventions that address the underlying reasons for the variability of clinical outcomes and will contribute to greater health equity.

Predicting the long-term effects of electronic cigarette use on population health: a systematic review of modelling studies

OBJECTIVE

To systematically review and synthesise the findings of modelling studies on the population impacts of e-cigarette use and to identify potential gaps requiring future investigation.

DATA SOURCE AND STUDY SELECTION

Four databases were searched for modelling studies of e-cigarette use on population health published between 2010 and 2023. A total of 32 studies were included.

DATA EXTRACTION

Data on study characteristics, model attributes and estimates of population impacts including health outcomes and smoking prevalence were extracted from each article. The findings were synthesised narratively.

DATA SYNTHESIS

The introduction of e-cigarettes was predicted to lead to decreased smoking-related mortality, increased quality-adjusted life-years and reduced health system costs in 29 studies. Seventeen studies predicted a lower prevalence of cigarette smoking. Models that predicted negative population impacts assumed very high e-cigarette initiation rates among non-smokers and that e-cigarette use would discourage smoking cessation by a large margin. The majority of the studies were based on US population data and few studies included factors other than smoking status, such as jurisdictional tobacco control policies or social influence.

CONCLUSIONS

A population increase in e-cigarette use may result in lower smoking prevalence and reduced burden of disease in the long run, especially if their use can be restricted to assisting smoking cessation. Given the assumption-dependent nature of modelling outcomes, future modelling studies should consider incorporating different policy options in their projection exercises, using shorter time horizons and expanding their modelling to low-income and middle-income countries where smoking rates remain relatively high.

Adherence to lifelines diet score and risk factors of metabolic syndrome among overweight and obese adults: A cross-sectional study

Background

Metabolic syndrome (MetS) is one of the most significant public health issues worldwide, and diet quality is an important controllable environmental factor influencing the incidence of MetS. Numerous dietary scores have been established to assess compliance with dietary recommendations or eating patterns, many of which are not entirely food-based. Hence, Lifelines Diet Score (LLDS) was developed in response to the shortcomings of existing tools. This study aimed to assess any possible links between total food quality and cardiometabolic risk factors among overweight and obese adults.

Methods

This cross-sectional study included 338 overweight and obese individuals [body mass index (BMI) > 25 kg/m2] aged 20-50 years in Tabriz, Iran. To collect dietary data, we used a validated semi-quantitative Food Frequency Questionnaire (FFQ) for Iranian population. Enzymatic-colorimetric methods were used to assess serum glucose and lipids, and enzyme-linked immunosorbent assay (ELISA) kits were used to measure insulin levels. In addition, the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) and Quantitative Insulin Sensitivity Check Index (QUICKI) were calculated.

Results

BMI and hip circumference (HC) were significantly different (P < 0.05) amongst LLDS tertiles. Adherence to the highest tertile of LLDS was associated with lower SBP, and the subjects in higher LLDS tertiles significantly had lower systolic blood pressure (SBP) (P = 0.04). Triglyceride (TG) levels were also lower in the third tertile of LLDS with a near-significant P-value (P = 0.05).

Conclusion

According to our results, a higher diet quality score, determined by LLDS, can be associated with a lower risk of MetS. Further experimental and longitudinal studies are needed to better understand this relationship.

Impact of the e-cigarette era on cigarette smoking among youth in the United States: A population-level study

To examine and compare trends in past 30-day cigarette smoking among adolescents in the US from 2002 to 2019, before and after the onset of the "e-cigarette era" in 2014. Using National Youth Tobacco Survey (NYTS) data from 2002 to 2019, we modeled the prevalence of past 30-day cigarette smoking over time. A total of n = 274,551 (weighted N = 340,403,754) middle and high school students were included in this study. Interrupted time series analyses were used to examine changes in cigarette smoking over time and compare trends in cigarette smoking pre- and post-2014. Models were applied to the full sample and stratified by middle (6th-8th grade) and high school (9th-12th grade). The observed number of current adolescent cigarette smokers post-2014 was compared to the predicted number, given trends in cigarette smoking prevalence observed pre-2014. Among all students, past 30-day cigarette smoking declined by approximately 0.75% per year from 2002 to 2013 (p < 0.001). Following a significant drop in prevalence from 2013 to 2014 (1.64%; p < 0.001), the decline in past 30-day cigarette smoking slowed significantly to approximately 0.37% per year (p < 0.001), from 2015 to 2019. We estimate that the onset of the "e-cigarette era" in 2014 corresponded to over 1.66 million (95% CI: 1.57 m - 1.75 m) more past 30-day cigarette smokers from 2015 to 2019, cumulatively. The rate of decline in past 30-day cigarette smoking prevalence among adolescents observed since 2002 slowed with the onset of the "e-cigarette era" in 2014, providing evidence at a population-level for the "gateway effect."

Population-level counterfactual trend modelling to examine the relationship between smoking prevalence and e-cigarette use among US adults

BACKGROUND

Studies have suggested that some US adult smokers are switching away from smoking to e-cigarette use. Nationally representative data may reflect such changes in smoking by assessing trends in cigarette and e-cigarette prevalence. The objective of this study is to assess whether and how much smoking prevalence differs from expectations since the introduction of e-cigarettes.

METHODS

Annual estimates of smoking and e-cigarette use in US adults varying in age, race/ethnicity, and sex were derived from the National Health Interview Survey. Regression models were fitted to smoking prevalence trends before e-cigarettes became widely available (1999-2009) and trends were extrapolated to 2019 (counterfactual model). Smoking prevalence discrepancies, defined as the difference between projected and actual smoking prevalence from 2010 to 2019, were calculated, to evaluate whether actual smoking prevalence differed from those expected from counterfactual projections. The correlation between smoking discrepancies and e-cigarette use prevalence was investigated.

RESULTS

Actual overall smoking prevalence from 2010 to 2019 was significantly lower than counterfactual predictions. The discrepancy was significantly larger as e-cigarette use prevalence increased. In subgroup analyses, discrepancies in smoking prevalence were more pronounced for cohorts with greater e-cigarette use prevalence, namely adults ages 18-34, adult males, and non-Hispanic White adults.

CONCLUSION

Population-level data suggest that smoking prevalence has dropped faster than expected, in ways correlated with increased e-cigarette use. This population movement has potential public health implications.

Anesthesiologists and the Other Pandemic: Tobacco Use

Tobacco use will kill a projected 1 billion people in the 21st century in one of the deadliest pandemics in history. Tobacco use disorder is a disease with a natural history, pathophysiology, and effective treatment options. Anesthesiologists can play a unique role in fighting this pandemic, providing both immediate (reduction in perioperative risk) and long-term (reduction in tobacco-related diseases) benefits to their patients who are its victims. Receiving surgery is one of the most powerful stimuli to quit tobacco. Tobacco treatments that combine counseling and pharmacotherapy (e.g., nicotine replacement therapy) can further increase quit rates and reduce risk of morbidity such as pulmonary and wound-related complications. The perioperative setting provides a great opportunity to implement multimodal perianesthesia tobacco treatment, which combines multiple evidence-based tactics to implement the four core components of consistent ascertainment and documentation of tobacco use, advice to quit, access to pharmacotherapy, and referral to counseling resources.