Switching Away From Smoking and Reduction in Cigarette Consumption Among U.S. Adult Purchasers of the JUUL System Across 24 Months Including Diverse Subpopulations Disproportionately Affected by Cigarette Smoking.

INTRODUCTION: 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. AIMS AND METHODS: AWS (age ≥ 21 years) were recruited following their first purchase of a JUUL Starter Kit in 2018. Participants who 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 per day among those who continued smoking) were calculated. Analyses focused on racial and 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 per 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 (eg, education, mental and physical health conditions; switch rate range: 42%-57%). CONCLUSIONS: AWS demonstrated progressively increasing switching rates over 2 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 2 years later (no past-30-day smoking, with most continuing to use ENDS). Furthermore, 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, eg, by ethnicity and income). ENDS can serve as an effective harm reduction strategy to complement current efforts to reduce tobacco-related disparities.

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.

Increased e-cigarette use prevalence is associated with decreased smoking prevalence among US adults.

BACKGROUND: If US adults who smoke cigarettes are switching to e-cigarettes, the effect may be observable at the population level: smoking prevalence should decline as e-cigarette prevalence increases, especially in sub-populations with highest e-cigarette use. This study aimed to assess such effects in recent nationally-representative data. METHODS: We updated a prior analysis with the latest available National Health Interview Survey data through 2022. Data were cross-sectional estimates of the yearly prevalence of smoking and e-cigarette use, respectively, among US adults and among specific age, race/ethnicity, and sex subpopulations. Non-linear models were fitted to observed smoking prevalence in the pre-e-cigarette era, with a range of 'cut-off' years explored (i.e., between when e-cigarettes were first introduced to when they became widely available). These trends were projected forward to predict what smoking prevalence would have been if pre-e-cigarette era trends had continued uninterrupted. The difference between actual and predicted smoking prevalence ('discrepancy') was compared to e-cigarette use prevalence in each year in the e-cigarette era to investigate whether the observed decline in smoking was statistically associated with e-cigarette use. RESULTS: Observed smoking prevalence in the e-cigarette era was significantly lower than expected based on pre-e-cigarette era trends; these discrepancies in smoking prevalence grew as e-cigarette use prevalence increased, and were larger in subpopulations with higher e-cigarette use, especially younger adults aged 18-34. Results were robust to sensitivity tests varying the analysis design. CONCLUSIONS: Population-level data continue to suggest that smoking prevalence has declined at an accelerated rate in the last decade in ways correlated with increased uptake of e-cigarette use.

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.

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.

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.

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.

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.

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.

The Relationship Between Electronic Cigarette Use and Conventional Cigarette Smoking Is Largely Attributable to Shared Risk Factors.

INTRODUCTION: The growing popularity of electronic cigarettes (e-cigarettes) among youth raises concerns about possible causal effects on conventional cigarette smoking. However, past research remains inconclusive due to heavy confounding between cigarette and e-cigarette use. This study uses propensity score methods to robustly adjust for shared risk in estimating the relationship between e-cigarette use and conventional smoking. METHODS: Cross-sectional data from 8th and 10th graders were drawn from the 2015-2016 waves of Monitoring the Future (n = 12 421). The effects of (1) lifetime and (2) current e-cigarette use on (A) lifetime and (B) current conventional cigarette smoking were examined using logistic regression analyses with inverse propensity weighting based on 14 associated risk factors. RESULTS: After accounting for the propensity for using e-cigarettes based on 14 risk factors, both lifetime and current e-cigarette use significantly increased the risk of ever smoking a conventional cigarette (OR = 2.49, 95% CI = 1.77 to 3.51; OR = 2.32, 95% CI = 1.66 to 3.25, respectively). However, lifetime (OR = 2.17, 95% CI = 0.62 to 7.63) and current e-cigarette use (OR = 0.95, 95% CI = 0.55 to 1.63) did not significantly increase the risk of current conventional cigarette smoking. CONCLUSIONS: E-cigarette use does not appear to be associated with current, continued smoking. Instead, the apparent relationship between e-cigarette use and current conventional smoking is fully explained by shared risk factors, thus failing to support claims that e-cigarettes have a causal effect on concurrent conventional smoking among youth. E-cigarette use has a remaining association with lifetime cigarette smoking after propensity score adjustment; however, future research is needed to determine whether this is a causal relationship or merely reflects unmeasured confounding. IMPLICATIONS: This study examines the relationship between e-cigarette use and conventional smoking using inverse propensity score weighting, an innovative statistical method that produces less-biased results in the presence of heavy confounding. Our findings show that the apparent relationship between e-cigarette use and current cigarette smoking is entirely attributable to shared risk factors for tobacco use. However, e-cigarette use is associated with lifetime cigarette smoking, though further research is needed to determine whether this is a causal relationship or merely reflects unaccounted-for confounding. Propensity score weighting produced significantly weaker effect estimations compared to conventional regression control.

Smoking is associated with a higher risk of unplanned medical visits among adult patients with diabetes, using retrospective electronic medical record data from 2014 to 2016.

BACKGROUND: Smoking exacerbates the complications of diabetes, but little is known about whether patients with diabetes who smoke have more unplanned medical visits than those who do not smoke. This study examines the association between smoking status and unplanned medical visits among patients with diabetes. METHODS: Data were drawn from electronic medical records (EMR's) from a large healthcare provider 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 2014-16 (N = 62,149). The association between smoking status (current, former, or never smoker) and having ≥1 unplanned visit (comprised of emergency department visits, hospitalizations, hospital observations, and urgent care) was examined after adjusting for age, race/ethnicity, and body mass index (BMI). The top ten most common diagnoses for unplanned visits were examined by smoking status. RESULTS: Both current and former smoking were associated with an approximately 1.2-fold increase in the odds of having at least one unplanned medical visit in the 3-year period (OR = 1.22, 95% CI = 1.16-129; OR = 1.23, 95% CI = 1.19-1.28, respectively), relative to never-smokers. Most common diagnoses for all patients were pain-related. However, diagnoses related to musculoskeletal system and connective tissue disorders were more common among smokers. Smoking is associated with a higher rate of unplanned medical visits among patients with diabetes in this regional healthcare system. CONCLUSIONS: Results from this study reveal higher rates of unplanned visits among smokers and former smokers, as well as increased frequencies of unplanned medical visits among current smokers.

Patterns of Polysubstance Use Among Non-Hispanic White and American Indian/Alaska Native Adolescents: An Exploratory Analysis.

INTRODUCTION: The prevalence of polysubstance use is well described, but less is known about correlates and patterns of polysubstance use. Previous research characterized latent subgroups of substance users by the type substance used. Racial disparities in the prevalence of polysubstance use exist, particularly for American Indian/Alaska Native (AI/AN) populations. The objective of our study was to describe differences in patterns of polysubstance use between non-Hispanic white and AI/AN adolescents. METHODS: We obtained data from the 2013 Youth Risk Behavior Survey (YRBS). We analyzed substance use patterns (cigarettes, alcohol, marijuana, cocaine, inhalants, heroin, methamphetamines, ecstasy, steroids, and injected drugs) separately among 375 AI/AN and 15,633 non-Hispanic white adolescents. We calculated pairwise correlations. Exploratory factor analysis identified latent factors of polysubstance use patterns. RESULTS: The use of all substances by AI/AN adolescents was the same or higher than use by non-Hispanic white adolescents, particularly for cocaine, heroin, and steroids. We found strong correlations between use of heroin and injected drugs and between use of cocaine and ecstasy among both populations. We found a latent factor for cigarettes, alcohol, and marijuana and another factor for broad polysubstance use among both populations. We found a factor for steroids and injected drugs among AI/AN adolescents, a factor for cocaine and ecstasy among non-Hispanic white adolescents, and a unique factor for methamphetamines. CONCLUSION: Differences in substance use patterns exist between AI/AN and non-Hispanic white adolescents, particularly for illegal drug use. If validated in future research, information on these differences could be used to inform tailored intervention programs aimed at preventing substance use.

The Role of Nicotine Dependence in E-Cigarettes' Potential for Smoking Reduction.

Introduction: E-cigarettes (Electronic Nicotine Delivery Systems, or ENDS) are an increasingly popular tobacco product among youth. Some evidence suggests that e-cigarettes may be effective for harm reduction and smoking cessation, although these claims remain controversial. Little is known about how nicotine dependence may contribute to e-cigarettes' effectiveness in reducing or quitting conventional smoking. Methods: A cohort of young adults were surveyed over 4 years (approximately ages 19-23). Varying-coefficient models (VCMs) were used to examine the relationship between e-cigarette use and conventional smoking frequency, and how this relationship varies across users with different nicotine dependence levels. Results: Lifetime, but not recent, e-cigarette use was associated with less frequent concurrent smoking of conventional cigarettes among those with high levels of nicotine dependence. However, nondependent e-cigarette users smoked conventional cigarettes slightly more frequently than those who had never used e-cigarettes. Nearly half of ever e-cigarette users reported using them to quit smoking at the last measurement wave. For those who used e-cigarettes in a cessation attempt, the frequency of e-cigarette use was not associated with reductions in future conventional smoking frequency. Conclusions: These findings offer possible support that e-cigarettes may act as a smoking reduction method among highly nicotine-dependent young adult cigarette smokers. However, the opposite was found in non-dependent smokers, suggesting that e-cigarette use should be discouraged among novice tobacco users. Additionally, although a substantial proportion of young adults used e-cigarettes to help them quit smoking, these self-initiated quit attempts with e-cigarettes were not associated with future smoking reduction or cessation. Implications: This study offers potential support for e-cigarettes as a smoking reduction tool among highly nicotine-dependent young adult conventional smokers, although the extent and nature of this remains unclear. The use of e-cigarettes as a quit aid was not associated with reductions in conventional smoking, consistent with most other quit aids in this sample except for nicotine replacement therapy, which was only effective for the most dependent smokers. Notably, these findings highlight the necessity of accounting for smokers' nicotine dependence levels when examining tobacco use patterns.

Earlier smoking after waking and the risk of asthma: a cross-sectional study using NHANES data.

BACKGROUND: Recent research shows that nicotine dependence conveys additional health risks above and beyond smoking behavior. The current study examines whether smoking within 5 min of waking, an indicator of nicotine dependence, is independently associated with asthma outcomes. METHODS: Data were drawn from five pooled cross-sectional waves (2005-14) of NHANES, and the final sample consisted of N = 4081 current adult smokers. Weighted logistic regressions were run examining the relationship between smoking within 5 min of waking and outcomes of lifetime asthma, past-year asthma, and having had an asthma attack in the past year. Control variables included demographics, smoking behavior, family history of asthma, depression, obesity, and secondhand smoking exposure. RESULTS: After adjusting for smoking behavior, smoking within 5 min was associated with an approximately 50% increase in the odds of lifetime asthma (OR = 1.46, p = .008) and past-year asthma (OR = 1.47, p = .024), respectively. After additionally adjusting for demographics and other asthma risk factors, smoking within 5 min of waking was associated with a four-fold increase in the odds of lifetime asthma (OR = 4.05, p = .015). CONCLUSIONS: Smoking within 5 min of waking, an indicator of nicotine dependence, is associated with a significantly increased risk of lifetime asthma in smokers. These findings could be utilized in refining risk assessment of asthma among smokers.

Integrative Data Analysis of Gender and Ethnic Measurement Invariance in Nicotine Dependence Symptoms.

Little research has evaluated whether conflicting evidence for gender and racial/ethnic differences in nicotine dependence (ND) may be attributed to differences in psychometric properties of ND symptoms, particularly for young Hispanic smokers. Inadequate racial/ethnic diversity and limited smoking exposure variability has hampered research in young smokers. We used integrative data analysis (IDA) to pool DSM-IV ND symptom data for current smokers aged 12-25 (N = 20,328) from three nationally representative surveys (1999, 2000 National Surveys on Drug Use and Health (NSDUH) and Wave 1 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Moderated nonlinear factor analysis (MNLFA) tested symptom measurement invariance in the pooled sample containing greater ethnic and smoking exposure variability. There was study noninvariance for most symptoms. NESARC participants were more likely to report tolerance, using larger amounts or for longer periods, inability to cut down/quit, and more time spent smoking at higher levels of ND severity, but reported emotional/physical health problems at lower ND severity. Four symptoms showed gender or race/ethnicity noninvariance, but observed differences were small. An ND severity factor score adjusting for symptom noninvariance related to study membership, gender, and race/ethnicity did not differ substantively from traditional DSM-IV diagnosis and number of endorsed symptoms in estimated gender and race/ethnicity differences in ND. Results were consistent with studies finding minimal gender and racial/ethnic differences in ND, and suggest that symptom noninvariance is not a major contributor to observed differences. Results support IDA as a potentially promising approach for testing novel ND hypotheses not possible in independent studies.

Time-Varying Effects of Parental Alcoholism on Depression.

INTRODUCTION: Children of alcoholic parents are at increased risk for lifetime depression. However, little is known about how this risk may change in magnitude across age, especially in mid-adulthood and beyond. METHODS: We used a nationally representative sample (N = 36,057) of US adults from the National Epidemiologic Survey on Alcohol and Related Conditions, wave III. After adjusting for demographic characteristics, we examined the relationship between parental alcoholism and outcomes of 1) major depressive disorder, Diagnostic and Statistical Manual of Mental Disorders-5th edition (DSM-5) and 2) DSM-5 persistent depressive disorder. To examine continuous moderation of this relationship across participants' age, we used time-varying effect models. RESULTS: Parental alcoholism was associated in general with a higher risk for both major depressive disorder (odds ratio [OR], 1.98, 95% confidence interval [CI], 1.85-2.11; P < .001) and persistent depressive disorder (OR, 2.28, 95% CI, 2.04-2.55; P < .001). The association between parental alcoholism and major depressive disorder was stable and positive across age, but the association with persistent depressive disorder significantly declined among older adults; respondents older than 73 years old were not at increased risk for persistent depressive disorder. CONCLUSIONS: Findings from this study show that the risk of parental alcoholism on depression is significant and stable among individuals of a wide age range, with the exception of a decline in persistent depressive risk among older adults. These findings highlight the importance of screening for depression among adults with parental alcoholism.

Time to First Cigarette, a Proxy of Nicotine Dependence, Increases the Risk of Pulmonary Impairment, Independently of Current and Lifetime Smoking Behavior.

INTRODUCTION: Cigarette smoking is the largest known risk factor for chronic obstructive pulmonary disease (COPD), but little is known about the role of time to first cigarette (TTFC), an indicator of nicotine dependence (ND). This study examines whether daily TTFC is associated with pulmonary outcomes, independently of smoking behavior. METHODS: A cross-sectional sample of 1461 current adult smokers were drawn from the National Health and Nutrition Examination Survey (NHANES), 2007-2010. The relationships of daily TTFC with outcomes of spirometry-defined pulmonary impairment and self-reported respiratory symptoms (coughing, bringing up phlegm, and wheezing) were examined (1) at the unadjusted level, (2) after adjusting for smoking heaviness and duration, and (3) after also adjusting for environmental exposure and demographics. RESULTS: In fully-adjusted weighted regressions, those reporting TTFC ≤ 5 minutes were three times as likely to have COPD (confidence interval [CI] = 1.30-8.77), had a 3% lower forced vital capacity expired in the first second (FEV1/FVC) (CI = -0.051 to -0.009), were seven times as likely to report coughing (CI = 1.96-26.41), and 16 times as likely to report bringing up phlegm (CI = 3.43-74.82), relative to those reporting TTFC > 60 minutes. Similar associations were often found when comparing TTFC between 5 to 30 minutes and TTFC between 30 to 60 minutes with TTFC > 60 minutes. CONCLUSIONS: "Addicted" smoking, as measured by earlier TTFC, is associated with a markedly increased risk of spirometry-measured obstructive pulmonary impairment, and of reporting symptoms of coughing and phlegm, even after controlling for smoking behavior and other risk factors for COPD. TTFC may prove valuable in more precisely assessing smokers' risk of pulmonary impairment. IMPLICATIONS: This study shows that smoking sooner after waking, a reliable indicator of ND, substantially increases the risk of spirometry-defined pulmonary impairment and self-reported symptoms, independently of lifetime and current smoking behavior. This study adds to a small body of literature examining health outcomes associated with higher ND, including outcomes of COPD. The current study overcomes important shortcomings of these existing studies in at least two ways: controlling for other known risk factors for COPD, and using empirical, spirometry-defined outcomes pulmonary function rather than self-reported COPD outcomes.