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.

Questionable Effects of Electronic Cigarette Use on Cardiovascular Diseases From the National Health Interview Survey (NHIS, 2014-2021).

BACKGROUND: Electronic cigarettes (e-cigarettes) and cardiovascular health risks have gained attention among tobacco researchers. While the cardiovascular risks from e-cigarettes are still unclear, a recent paper by Alzahrani in Cureus claimed that current usage of e-cigarettes increases the risks of cardiovascular diseases, such as myocardial infarction and stroke, in subjects who were never cigarette smokers. METHODS: The National Health Interview Survey (NHIS) data from 2014 to 2021 and logistic regression models were used to replicate and extend Alzahrani's analysis. RESULTS: Only 12 never smokers who were current e-cigarette users had a myocardial infarction in all eight years. The crude odds ratio (OR) for e-cigarette use was 0.42 (95%CI: 0.24, 0.75). After adjusting for age and other confounding factors and health conditions, the OR of e-cigarette use increased to 2.48 (95%CI: 1.35, 4.55). The omission of age while adjusting for all other risk factors resulted in an OR of 0.80 (95%CI: 0.45, 1.43). In addition, the adjusted ORs for coronary heart disease and stroke were 1.12 (95%CI: 0.58, 2.17) and 1.13 (95%CI: 0.55, 2.29), respectively. CONCLUSIONS: The findings indicate that Alzahrani's study is scientifically unreliable. The association between e-cigarette use and heart attack reported by Alzahrani was substantially driven by age, and the very small number of exposed cases makes the association very unstable. Given the nature of cross-sectional NHIS data, it is impossible to establish a robust association or causal claim that e-cigarette use "increases" the risks of any disease.

Switching Away From Smoking and Reduction in Cigarette Consumption among US Adult Purchasers of the JUUL System across 24 Months Including Diverse Subpopulations Disproportionately Affected by Cigarette Smoking.

OBJECTIVES: Electronic nicotine delivery systems (ENDS) can reduce tobacco-related health risks for adults who smoke cigarettes (AWS) by facilitating complete switching away from cigarettes. However, little is known about ENDS use and switching among subpopulations that have been disproportionately affected by smoking. METHODS: AWS (age≥21 years) were recruited following their first purchase of a JUUL Starter Kit in 2018. Participants self-reported switching (no past-30-day cigarette smoking) at 1-, 2-, 3-, 6-, 9-, 12-, 15-, 18-, 21-, and 24-month follow-ups. Percent switched and percent with substantial smoking reduction (≥50% decrease in cigarettes/day among those who continued smoking) were calculated. Analyses focused on racial/ethnic minorities, persons with low income and education levels, sexual minorities, and those with mental and physical health conditions. RESULTS: Overall rates of switching away from cigarettes increased across follow-ups to 51.2% (Month-12) to 58.6% (Month-24, 87% of whom used ENDS). Among those who continued to smoke at 24 months, 45.4% reduced cigarettes/day by ≥50%. Rates of switching and substantial smoking reduction were largely similar across subgroups, with some statistically significant, but small, differences in Month-24 switching rates(e.g. education, mental/physical health conditions; switch rate range: 42~57%). CONCLUSIONS: AWS demonstrated progressively increasing switching rates over two years after purchasing JUUL products. Similar trends in switching and smoking reduction were observed across populations disproportionately affected by smoking. By facilitating switching and smoking reduction, ENDS products such as JUUL may provide an opportunity to reduce smoking-related harm among some populations disproportionately affected by smoking, potentially reducing tobacco-related health disparities. IMPLICATIONS: ENDS have the potential to benefit population health if they can replace cigarettes. This benefit must extend to populations disproportionately affected by smoking. In this real-world study, 59% of JUUL purchasers reported complete switching two years later (no past-30-day smoking, with most continuing to use ENDS). Further, 45% of those who continued to smoke reduced cigarette consumption by at least half. These rates of switching and smoking reduction were largely comparable across populations disproportionately affected by smoking (defined, e.g., by ethnicity and income). ENDS can serve as an effective harm reduction strategy to complement current efforts to reduce tobacco-related disparities.

What Substances Are Adolescents Vaping? Estimating Nicotine-Specific and Cannabis-Specific Vaping from US National Youth Surveys.

BACKGROUND: Youth use of electronic cigarettes ("e-cigarettes") is an ongoing concern. Vaping is usually assumed to be of nicotine, but survey responses may also include vaping of non-nicotine substances (particularly cannabis), which can impose different risks. The current study quantifies the proportions of nicotine-specific and cannabis-specific vaping among adolescents. METHODS: Data from the Monitoring the Future (MTF) 2021 survey were analyzed with respect to the substance vaped: nicotine, cannabis, or flavoring only. Past-30-day (P30D) nicotine-specific vaping (exclusively, or also having vaped other substances) and cannabis-specific vaping were calculated. Results were also broken down by grade level, cigarette smoking history (current, former, or never), and frequency of e-cigarette use (<20 vs. 20+ days in P30D). RESULTS: Among 4706 P30D e-cigarette users, 84.2% vaped nicotine; the remaining 15.8% reported not vaping any nicotine in P30D. Cannabis vaping was reported by 48.4% of P30D vapers. Vaping both nicotine and cannabis in P30D was more common in current and former smokers than in never-smokers, and in frequent vapers (of any substance). Conversely, never-smokers were more commonly vaping cannabis but not nicotine, compared to current and former smokers. Supplementary NYTS 2022 analyses were consistent with these findings. CONCLUSIONS: A notable proportion of self-reported P30D vapers reported not vaping nicotine; nearly half vaped cannabis. Capturing the variety of substances used in vaping devices is imperative for accurate public health surveillance of both nicotine and cannabis vaping among US adolescents, considering their different respective harms and regulatory frameworks.

Comparative risk perceptions of switching to JUUL vs. continued smoking and subsequent switching away from cigarettes: a longitudinal observational study.

BACKGROUND: Evidence indicates that electronic nicotine delivery systems (ENDS) pose lower risk than cigarettes; however, many smokers harbor misperceptions that ENDS are equally or more harmful, possibly deterring them from switching. This study examines whether comparative risk perceptions of JUUL vs. smoking are associated with subsequent switching, among smokers who recently purchased JUUL. METHODS: N = 16,996 current established smokers who recently purchased a JUUL Starter Kit were followed 6 times over 12 months. Comparative risk perceptions were assessed using both direct and indirect measures (i.e., contrasting JUUL and smoking directly in questions, and deriving from separate absolute scales). Repeated-measures logistic regression examined switching across follow-up (no smoking in past 30 days) as a function of baseline risk perceptions, adjusting for demographics and baseline smoking behavior. RESULTS: Perceiving JUUL as less harmful than smoking was associated with higher switching rates, using both direct (e.g., adjusted odds ratio [AOR] = 1.48 for "JUUL much less" vs. "more/much more harmful") and indirect (AOR = 1.07, for each 10-unit increase in fraction; AOR = 1.51 for highest (6-100) vs. lowest (0 to < 1) fraction categories) comparative risk measures (all p < 0.0001). Among the subset smoking 10 + cigarettes per day, associations between risk perceptions and switching were more pronounced (AOR = 2.51 for "JUUL much less" vs. "more/much more harmful"; AOR = 1.81 for 6-100 vs. 0 to < 1 fraction, both p < 0.0001). CONCLUSIONS: Smokers who perceive JUUL as less harmful than cigarettes have higher odds of switching. Future research should examine whether messaging which aligns comparative risk perceptions with current evidence can facilitate switching, especially among heavier smokers.

Youth patterns of use of electronic nicotine delivery systems (ENDS), Population Assessment of Tobacco and Health (PATH) waves 4-5.5.

INTRODUCTION: Youth use of electronic nicotine delivery systems (ENDS) is a continuing concern, making it important to assess evolving patterns, especially as non-tobacco, non-menthol (NTM) flavors were withdrawn for pod-based (but not disposable) ENDS in February 2020. METHODS: Trends in past-30-day (P30D) ENDS use and smoking prevalence, usual device type, flavor (tobacco, mint/menthol, or fruit/sweet/other), and regular/last-used brand in PATH Waves 4 (2017), 4.5 (2018), 5 (2019), and 5.5 (2020) were examined. Shifts between 2019 and 2020 in flavor use for pods and disposables were examined. RESULTS: P30D ENDS use peaked in 2019 at 8.6 % of all youth, subsequently declining by nearly half to 4.5 % in 2020. Meanwhile, P30D cigarette smoking declined to an all-time low (1.3 %) in 2020. Within this overall decline, consumption shifted to disposable ENDS, which increased nearly 10-fold (from 5.0 % to 49.2 % of P30D ENDS users). Relatedly, use of fruit/sweet/other flavors remained similar overall between 2019 and 2020 (approximately 75-80 % of P30D ENDS users), but the use of these flavors became concentrated in disposable ENDS in 2020 (a 12-fold increase from 4.4 % to 58.4 % of fruit/sweet/other-flavor users). CONCLUSIONS: PATH results show similar trends to other US national surveys in youth ENDS trends. The removal of non-tobacco, non-menthol flavors in pod-based ENDS (while remaining available in disposables) has likely driven youth towards disposable devices, resulting in continued high use of fruit/sweet/other flavors, which are now predominant in users of disposable ENDS. Wave 5.5 is uninformative regarding brand use because common disposable brands were not queried.

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.

Weekend delivery and maternal-neonatal adverse outcomes in low-risk pregnancies in the United States: A population-based analysis of 3-million live births.

BACKGROUND: Childbirth is the most common cause of hospital admission in the United States. Previous studies have shown that there might be a "weekend effect" in perinatal care, indicating that mothers and newborns whose deliveries occur during the weekends are at increased risk of having adverse outcomes. This study aims to isolate the association between the weekend delivery and maternal-neonatal adverse outcomes by investigating low-risk pregnancies in nationwide data. METHODS: A population-based study of all low-risk pregnancies (in-hospital, nonanomalous, term, normal birthweight, and singleton) was conducted based on US national natality data in 2017. Four maternal outcomes (ICU admission, uterine rupture, blood transfusion, and perineal laceration) and three neonatal outcomes (5-minute Apgar <7, NICU admission, and neonatal death) were defined as adverse outcomes. Logistic regression analyses were conducted to determine the association, adjusting for 23 maternal and neonatal characteristics and risk factors. RESULTS: Among 3 011 577 low-risk pregnancies, 6.0% were reported to have at least one of the maternal-neonatal adverse outcomes. Weekend deliveries were significantly associated with six maternal-neonatal adverse outcomes with an exception of neonatal death. In general, weekend deliveries were 1.13 times significantly as likely to have any of seven maternal-neonatal adverse outcomes than weekday deliveries (OR 1.13, 95% CI 1.11-1.14), being attributed to adverse outcomes of more than 4500 mother-newborn pairs. CONCLUSIONS: Weekend delivery is a consistent risk factor for both mothers and babies at the national level. Furthermore, studies are needed about possible modifiable factors that mediate these associations to ensure safe childbirth regardless of the day of delivery.

Analysis of common methodological flaws in the highest cited e-cigarette epidemiology research.

The prevalence of vaping, also known as using e-cigarettes, vapes and vape pens, has prompted a demand for reliable, evidence-based research. However, published literature on the topic of vaping often raises concerns, characterized by serious flaws and a failure to adhere to accepted scientific methodologies. In this narrative review, we analyze popular vaping studies published in medical journals that purport to evaluate the association of vaping and smoking cessation, smoking initiation or health outcomes. We analyzed 24 included studies to identify the questions they claimed to address, stated methods, manner of implementation, discussions, and stated conclusions. After critical appraisal, we noted a multiplicity of flaws in these studies, and identified patterns as to the nature of such flaws. Many studies lacked a clear hypothesis statement: to the extent that a hypothesis could be inferred, the methods were not tailored to address the question of interest. Moreover, main outcome measures were poorly identified, and data analysis was further complicated by failure to control for confounding factors. The body of literature on "gateway" theory for the initiation of smoking was particularly unreliable. Overall, the results and discussion contained numerous unreliable assertions due to poor methods, including data collection that lacked relevance, and assertions that were unfounded. Many researchers claimed to find a causal association while not supporting such findings with meaningful data: the discussions and conclusions of such studies were, therefore, misleading. Herein, we identify the common flaws in the study design, methodology, and implementation found in published vaping studies. We present our summary recommendations for future vaping research. Our aim is to prompt future researchers to adhere to scientific methods to produce more reliable findings and conclusions in the field of vaping research.

Rural disparities in adolescent smoking prevalence.

INTRODUCTION: Rural residence is a well-established risk factor for risk behaviors and subsequent morbidity and mortality in the United States. Smoking is the primary cause of preventable death and is more prevalent in rural America. As chronic smoking habits typically develop during adolescence, the discrepancy in smoking rates between rural and urban youth likely contributes to a significant geographic disparity in the long-term health of adults. METHODS: Data were extracted from 12th-grader surveys of the US Monitoring the Future study from 1998 to 2018. The historic trends of smoking initiation, ever-regular and current-regular smoking rates of rural and urban adolescents were estimated with intercept-only time-varying effect models. Differences in prevalence between rural and urban youth were calculated for each smoking behavior. RESULTS: Though overall smoking prevalence continues to decline, this trend is significantly attenuated among rural adolescents compared to urban youth. The absolute difference in lifetime smoking prevalence between rural and urban youth has markedly increased from 6.9% in 1998 to 13.5% in 2018, which is among the highest in the past 20 years and is a potentially alarming upward trend. However, the absolute differences in ever-regular and current-regular smoking prevalence have shown an overall net decline, decreasing from 6.4 to 4.8%, and from 5.5 to 3.0%, respectively. CONCLUSIONS: This geographic disparity between rural and urban adolescents represents a potentially modifiable cause of increased morbidity and mortality in rural areas. Interventions and regulatory efforts should be tailored for rural adolescents to reduce the narrowing but persistent disparity in regular smoking.

Using Electronic Medical Records and Health Claim Data to Develop a Patient Engagement Score for Patients With Multiple Chronic Conditions: An Exploratory Study.

The study objective was to (1) develop a statistical model that creates a novel patient engagement score (PES) from electronic medical records (EMR) and health claim data, and (2) validate this developed score using health-related outcomes and charges of patients with multiple chronic conditions (MCCs). This study used 2014-16 EMR and health claim data of patients with MCCs from Sanford Health. Patient engagement score was created based on selected patients' engagement behaviors using Gaussian finite mixture model. The PES was validated using multiple logistic and linear regression analyses to examine the associations between the PES and health-related outcomes, and hospital charges, respectively. Patient engagement score was generated from 5095 patient records and included low, medium, and high levels of patient engagement. The PES was a significant predictor for low-density lipoprotein, emergency department visit, hemoglobin A1c, estimated glomerular filtration rate, hospitalization, and hospital charge. The PES derived from patient behaviors recorded in EMR and health claim data can potentially serve as a patient engagement measure. Further study is needed to refine and validate the newly developed score.

Estimating causal and time-varying effects of maternal smoking on youth smoking.

INTRODUCTION: Maternal smoking is a well-known risk factor for youth smoking, yet whether this relationship is causal remains unresolved. This study utilizes propensity score methods for causal inference to robustly account for shared risk factors between maternal and offspring smoking. METHODS: An 8-year longitudinal cohort of 900 adolescents in the Chicago area were followed starting from approximately age 15.6. The effects of maternal lifetime smoking (MLS) and prenatal tobacco exposure (PTE) (among participants reporting MLS) on offspring's past 30-day smoking, daily smoking status and smoking frequency were examined using logistic regression and Poisson regression after nearest-neighbor propensity matching. Age dependency of this relationship was then examined across the age range of 15-25 using time-varying effect modeling. RESULTS: Propensity matching yielded 438 and 132 pairs for MLS and PTE study samples, respectively. MLS demonstrated significant associations with past 30-day smoking (RR 1.09; 95% CI 1.04-1.14), daily smoking (RR 1.08; 95% CI 1.05-1.12), and smoking frequency of offspring (RR 1.32; 95% CI 1.15-1.52), with stable effects across age. Among participants reporting MLS, having PTE showed significant additional effects on daily smoking (RR 1.09; 95% CI 1.02-1.17) and age-dependency that showed significance during young adulthood but not adolescence. CONCLUSION: The relationship between maternal and offspring smoking was not fully accounted for by shared risk factors, suggesting possible causation with PTE having a delayed effect across age. Targeted prevention efforts should be made on maternal smoking-exposed adolescents to mitigate their risks of developing heavy smoking habits in adulthood.

Predicting unplanned medical visits among patients with diabetes: translation from machine learning to clinical implementation.

BACKGROUND: Diabetes is a medical and economic burden in the United States. In this study, a machine learning predictive model was developed to predict unplanned medical visits among patients with diabetes, and findings were used to design a clinical intervention in the sponsoring healthcare organization. This study presents a case study of how predictive analytics can inform clinical actions, and describes practical factors that must be incorporated in order to translate research into clinical practice. METHODS: Data were drawn from electronic medical records (EMRs) from a large healthcare organization in the Northern Plains region of the US, from adult (≥ 18 years old) patients with type 1 or type 2 diabetes who received care at least once during the 3-year period. A variety of machine-learning classification models were run using standard EMR variables as predictors (age, body mass index (BMI), systolic blood pressure (BP), diastolic BP, low-density lipoprotein, high-density lipoprotein (HDL), glycohemoglobin (A1C), smoking status, number of diagnoses and number of prescriptions). The best-performing model after cross-validation testing was analyzed to identify strongest predictors. RESULTS: The best-performing model was a linear-basis support vector machine, which achieved a balanced accuracy (average of sensitivity and specificity) of 65.7%. This model outperformed a conventional logistic regression by 0.4 percentage points. A sensitivity analysis identified BP and HDL as the strongest predictors, such that disrupting these variables with random noise decreased the model's overall balanced accuracy by 1.3 and 1.4 percentage points, respectively. These recommendations, along with stakeholder engagement, behavioral economics strategies, and implementation science principles helped to inform the design of a clinical intervention targeting behavioral changes. CONCLUSION: Our machine-learning predictive model more accurately predicted unplanned medical visits among patients with diabetes, relative to conventional models. Post-hoc analysis of the model was used for hypothesis generation, namely that HDL and BP are the strongest contributors to unplanned medical visits among patients with diabetes. These findings were translated into a clinical intervention now being piloted at the sponsoring healthcare organization. In this way, this predictive model can be used in moving from prediction to implementation and improved diabetes care management in clinical settings.

The Effect of Maternal Smoking on Offspring Smoking Is Unrelated to Heritable Personality Traits or Initial Subjective Experiences.

INTRODUCTION: Maternal smoking is a risk factor for offspring smoking. Lifetime maternal smoking vs. prenatal tobacco exposure (PTE) appears to act through different mechanisms. This study tested the hypothesis that maternal smoking measures' effects on offspring smoking could be attributable to hereditary mechanisms: personality traits (novelty-seeking, impulsivity, neuroticism, and self-esteem) and initial subjective smoking experiences (pleasurable, unpleasurable, and dizziness). METHODS: Data were drawn from the Social and Emotional Contexts of Adolescent Smoking Patterns study, an 8-year longitudinal study of 9th or 10th graders at baseline (≈age 15) who experiment with smoking (<100 lifetime cigarettes; N = 594) at baseline. The young adult smoking frequency at the 8-year follow-up (≈age 23) was examined as a function of baseline characteristics (heritable trait, maternal smoking, PTE, and sex) and baseline smoking frequency and nicotine dependence. Structural equation models determined whether the inclusion of each heritable trait among offspring confounded the effects of maternal smoking (PTE or maternal smoking) on offspring smoking and nicotine dependence. RESULTS: Impulsiveness was associated with intermediate adolescent smoking frequency (B = 0.135, SD = 0.043, p = .002) and nicotine dependence (B = 0.012, SD = 0.003, p < .001). Unpleasurable first experience (B = 0.886, SD = 0.374, p = .018) and dizziness (B = 0.629, SD = 0.293, p = .032) showed a trend with intermediate smoking frequency that was nonsignificant after correcting for multiple comparisons. These traits did not confound maternal smoking's effects. CONCLUSIONS: None of the heritable traits examined in this model explained the effect of maternal smoking measures on adolescence or young adulthood offspring smoking. Further research is needed to elucidate the mechanism by which PTE and maternal smoking are linked to offspring smoking. IMPLICATIONS: Prenatal tobacco exposure (PTE) and mother's lifetime smoking present separate and independent risks for offspring smoking; however, their mechanisms seem unrelated to heritable personality traits and initial subjective smoking experiences. These findings have implications for separate screening strategies tailored to different age groups, especially related to PTE's risk of smoking in young adulthood. Additionally, these findings add to the known risks of maternal smoking. Further research is needed to understand the mechanism underlying the risk posed by maternal lifetime smoking and PTE on offspring smoking behavior.

Dual Use of Cigarettes and JUUL: Trajectory and Cigarette Consumption.

Objectives: Many smokers who start using ENDS engage in dual use of cigarettes and ENDS. This paper examines time trends in dual use after a JUUL Starter Kit purchase, and changes in cigarette consumption among those remaining dual users. Methods: In the ADJUSST study, a cohort of adult smokers who purchased a JUUL Starter Kit were followed naturalistically 6 times over 12 months. Trends in dual use and reported complete switching away from cigarettes (ie, no past 30-day smoking), and changes in daily average cigarettes per day from baseline at 6 and 12 months were examined. Results: Dual use declined over time as complete switching increased. By 12 months, 43.2% reported dual using, and 51.2% reported switching away from cigarettes. Among dual users, approximately 60% reduced their cigarette consumption substantially (by ≥ 50%); average reductions in cigarette consumption were > 80%. Only a small minority (3%-10%) substantially increased cigarette consumption. Conclusions: Dual use of smoking and JUUL declines over time as switching away from smoking increases. This suggests that, for most smokers, dual use is a transitional state leading to switching completely away from cigarettes. Furthermore, dual use is often marked by substantial reductions in cigarette consumption.

Reducing the smoking-related health burden in the USA through diversion to electronic cigarettes: a system dynamics simulation study.

BACKGROUND: Electronic cigarettes ("e-cigarettes") have altered tobacco smoking trends, and their impacts are controversial. Given their lower risk relative to combustible tobacco, e-cigarettes have potential for harm reduction. This study presents a simulation-based analysis of an e-cigarette harm reduction policy set in the USA. METHODS: A system dynamics simulation model was constructed, with separate aging chains representing people in different stages of use (both of combustible cigarettes and e-cigarettes). These structures interact with a policy module to close the gap between actual (simulated) and goal numbers of individuals who smoke, chosen to reduce the tobacco-attributable death rate (i.e., mostly combustible cigarette-attributable, but conservatively allowing e-cigarette-attributable deaths) to that due to all accidents in the general population. The policy is two-fold, removing existing e-liquid flavor bans and providing an informational campaign promoting e-cigarettes as a lower-risk alternative. Realistic practical implementation challenges are modeled in the policy sector, including time delays, political resistance, and budgetary limitations. Effects of e-cigarettes on tobacco smoking occur through three mechanisms: (1) diversion from ever initiating smoking; (2) reducing progression to established smoking; and (3) increasing smoking cessation. An important unintended effect of possible death from e-cigarettes was conservatively included. RESULTS: The base-case model replicated the historical exponential decline in smoking and the exponential increase in e-cigarette use since 2010. Simulations suggest tobacco smoking could be reduced to the goal level approximately 40 years after implementation. Implementation obstacles (time delays, political resistance, and budgetary constraints) could delay and weaken the effect of the policy by up to 62% in the worst case, relative to the ideal-case scenario; however, these discrepancies substantially decreased over time in dampened oscillations as negative feedback loops stabilize the system after the one-time "shock" introduced by policy changes. CONCLUSIONS: The simulation suggests that the promotion of e-cigarettes as a harm-reduction policy is a viable strategy, given current evidence that e-cigarettes offset or divert from smoking. Given the strong effects of implementation challenges on policy effectiveness in the short term, accurately modeling such obstacles can usefully inform policy design. Ongoing research is needed, given continuing changes in e-cigarette use prevalence, new policies being enacted for e-cigarettes, and emerging evidence for substitution effects between combustible cigarettes and e-cigarettes.

Alcohol Use Disorder Symptoms Rather than Alcohol Exposure Predict Experiences of Cannabis Use Disorder Symptoms among Adolescents and Young Adults.

Background: Alcohol and cannabis use are highly comorbid. Objective: We evaluate if alcohol use and/or alcohol use disorder symptoms predict experiences of cannabis use disorder symptoms among adolescents and young adults and whether the relationships differ based on frequency of cannabis use, recency of cannabis initiation and age. Method: Data were drawn from five annual surveys of the National Survey on Drug Use and Health (2014-2018) to include adolescents and young adults (age 12 to 21 years) who reported using cannabis and alcohol at least once in the past 30 days. Results: Number of alcohol use disorder symptoms, over and above alcohol quantity or frequency, was positively associated with each of the cannabis use disorder symptoms as well as the total number of cannabis use disorder symptoms endorsed. The association between alcohol and cannabis use disorder symptoms was stronger among those who were younger and those who initiated cannabis use within the past 2 years. Conclusions: Treatment should consider these and other cross-over effects of substance disorder symptoms when addressing risk for chronic and dependent use.

Trends in electronic cigarette use and conventional smoking: quantifying a possible 'diversion' effect among US adolescents.

BACKGROUND AND AIMS: The impact of electronic cigarettes (ECs) on nicotine use is hotly debated: some fear that ECs are a 'catalyst' to conventional smoking, while others argue that they divert adolescents from the more harmful product. This study used simulation modeling to evaluate the plausibility of catalyst and diversion hypotheses against real-world data. DESIGN: A simulation model represented life-time exclusive EC use, exclusive conventional smoking and dual use as separate subpopulations. The 'catalyst' effect was modeled as EC use increasing dual use initiation (i.e. EC users also start smoking). The 'diversion' effect was modeled as EC use decreasing exclusive cigarette initiation. The model was calibrated using data from the US National Youth Tobacco Survey (NYTS). The plausibility of each scenario was evaluated by comparing simulated trends with NYTS data. This is the first study, to our knowledge, to estimate the magnitude of a diversion effect through simulation. SETTING: United States. PARTICIPANTS AND MEASUREMENTS: Adolescents aged 12-17 years in NYTS, a cross-sectional study from 2000 to 2019 (n = 12 500 to 31 000 per wave). Exclusive cigarette use, exclusive EC use and dual use of both products were defined using cumulative life-time criteria (100+ cigarettes smoked and/or > 100 days vaped). FINDINGS: A null model (no catalyst or diversion) over-predicts NYTS smoking by up to 87%. Under the conservative assumption that the catalyst effect accounts for all dual use, an exponential decay constant of 19.6% EC users/year initiating smoking is required; however, this further over-predicts actual smoking by up to 109%. A diversion effect with an exponential decay constant of 55.4%/year or 65.4%/year, with the maximum possible opposing catalyst effect also active, is required optimally to match NYTS smoking trends (root mean square error = 286 632 versus 391 396 in the null model). CONCLUSIONS: A simulation model shows that a substantial diversion effect is needed to explain observed nicotine use trends among US adolescents, and it must be larger than any possible opposing catalyst effect, if present.