Pleasure and Satisfaction as Predictors of Future Cigarette and E-Cigarette Use: A Novel Two-Stage Modeling Approach.

INTRODUCTION: Subjective experience of e-cigarettes may be an important factor in helping people who use combustible cigarettes switch completely to e-cigarettes to reduce harms from smoking. This paper describes a novel two-stage analysis using pleasure and satisfaction responses from Ecological Momentary Assessments (EMA) of both cigarette and e-cigarette use to predict future cigarette and e-cigarette tobacco use. METHODS: This observational study included adult users of cigarettes and e-cigarettes who provided 7-days of EMA, capturing cigarette and e-cigarette use, followed by biweekly reports of cigarette and e-cigarette use over one year. Participants were 279 adults who provided both cigarette and e-cigarette responses during the EMA. We employed a two-stage analytic approach in which EMA data were used to predict subsequent levels of cigarette and e-cigarette use. In the first stage, EMA responses of cigarette and e-cigarette events were modeled via a mixed-effects location scale (MELS) model to yield summaries of participants' means and variability on event-related ratings of pleasure and satisfaction. These EMA summaries served as predictors in the second stage analysis of the biweekly post-EMA longitudinal cigarette and e-cigarette use data. RESULTS: EMA pleasure and satisfaction ratings were similar for both products and predicted both longitudinal cigarette and e-cigarette use, even after controlling for baseline cigarette and e-cigarette dependence. Relatively higher levels of satisfaction with e-cigarettes were associated with greater decreases in cigarette use over time. CONCLUSIONS: Pleasure and satisfaction are important predictors of subsequent cigarette and e-cigarette use. IMPLICATIONS: Experienced subjective pleasure and satisfaction from e-cigarettes relative to cigarettes may be an important factor in helping individuals who smoke to switch completely to e-cigarettes as a harm reduction approach. In order to help sustain complete product switching and reduce dual use or relapse to smoking, e-cigarettes may need to deliver more satisfaction to the user compared to that experienced from cigarettes.

What to do after smoking relapse? A sequential multiple assignment randomized trial of chronic care smoking treatments.

AIM: To compare effects of three post-relapse interventions on smoking abstinence. DESIGN: Sequential three-phase multiple assignment randomized trial (SMART). SETTING: Eighteen Wisconsin, USA, primary care clinics. PARTICIPANTS: A total of 1154 primary care patients (53.6% women, 81.2% White) interested in quitting smoking enrolled from 2015 to 2019; 582 relapsed and were randomized to relapse recovery treatment. INTERVENTIONS: In phase 1, patients received cessation counseling and 8 weeks nicotine patch. Those who relapsed and agreed were randomized to a phase 2 relapse recovery group: (1) reduction counseling + nicotine mini-lozenges + encouragement to quit starting 1 month post-randomization (preparation); (2) repeated encouragement to quit starting immediately post-randomization (recycling); or (3) advice to call the tobacco quitline (control). The first two groups could opt into phase 3 new quit treatment [8 weeks nicotine patch + mini-lozenges plus randomization to two treatment factors (skill training and supportive counseling) in a 2 × 2 design]. Phase 2 and 3 interventions lasted ≤ 15 months. MEASUREMENTS: The study was powered to compare each active phase 2 treatment with the control on the primary outcome: biochemically confirmed 7-day point-prevalence abstinence 14 months post initiating phase 2 relapse recovery treatment. Exploratory analyses tested for phase 3 counseling factor effects. FINDINGS: Neither skill training nor supportive counseling (each on versus off) increased 14-month abstinence rates; skills on versus off 9.3% (14/151) versus 5.2% (8/153), P = 0.19; support on versus off 6.6% (10/152) versus 7.9% (12/152), P = 0.73. Phase 2 preparation did not produce higher 14-month abstinence rates than quitline referral; 3.6% (8/220) versus 2.1% [3/145; risk difference = 1.5%, 95% confidence interval (CI) = -1.8-5.0%, odds ratio (OR) = 1.8, 95% CI = 0.5-6.9]. Recycling, however, produced higher abstinence rates than quitline referral; 6.9% (15/217) versus 2.1% (three of 145; risk difference, 4.8%, 95% CI = 0.7-8.9%, OR = 3.5, 95% CI = 1.0-12.4). Recycling produced greater entry into new quit treatment than preparation: 83.4% (181/217) versus 55.9% (123/220), P < 0.0001. CONCLUSIONS: Among people interested in quitting smoking, immediate encouragement post-relapse to enter a new round of smoking cessation treatment ('recycling') produced higher probability of abstinence than tobacco quitline referral. Recycling produced higher rates of cessation treatment re-engagement than did preparation/cutting down using more intensive counseling and pharmacotherapy.

An Introduction and Practical Guide to Strategies for Analyzing Longitudinal Data in Clinical Trials of Smoking Cessation Treatment: Beyond Dichotomous Point-Prevalence Outcomes.

Conceptualizing tobacco dependence as a chronic relapsing condition suggests the need to use analytic strategies that reflect that premise. However, clinical trials for smoking cessation typically define the primary endpoint as a measure of abstinence at a single timepoint distal to the intervention, typically 3-12 months. This reinforces the concept of tobacco outcomes as a dichotomous state-one is, or is not, abstinent. Fortunately, there are several approaches available to handle longitudinal data that reflect the relapsing and remitting nature of tobacco use during treatment studies. In this paper, sponsored by the Society for Research on Nicotine and Tobacco's Treatment Research Network, we present an introductory overview of these techniques and their application in smoking cessation clinical trials. Topics discussed include models to examine abstinence outcomes (eg, trajectory models of abstinence, models for transitions in smoking behavior, models for time to event), models that examine reductions in tobacco use, and models to examine joint outcomes (eg, examining changes in the use of more than one tobacco product). Finally, we discuss three additional relevant topics (ie, heterogeneity of effects, handling missing data, and power and sample size) and provide summary information about the type of model that can be used based on the type of data collected and the focus of the study. We encourage investigators to familiarize themselves with these techniques and use them in the analysis of data from clinical trials of smoking cessation treatment. Implications Clinical trials of tobacco dependence treatment typically measure abstinence 3-12 months after participant enrollment. However, because smoking is a chronic relapsing condition, these measures of intervention success may not accurately reflect the common trajectories of tobacco abstinence and relapse. Several analytical techniques facilitate this type of outcome modeling. This paper is meant to be an introduction to these concepts and techniques to the global nicotine and tobacco research community including which techniques can be used for different research questions with visual summaries of which types of models can be used for different types of data and research questions.

Parameters of EMA Compliance and Self-Reported Reactivity in a Longitudinal Study of Young Adult Cannabis and Tobacco Co-Users.

Background: Co-use of cannabis and tobacco has become increasingly popular among young adults. Interactive voice response (IVR) based ecological momentary assessment (EMA) allows for measurement of behavior in or near real-time, but has limitations including non-compliance, missing data, and potential for reactivity (e.g., behavior change) from frequent assessments. Methods: This study examined tobacco and cannabis use characteristics and factors associated with IVR compliance and self-reported reactivity in 97 young adults who reported cannabis and tobacco co-use at baseline and completed daily IVR surveys of co-use behavior at three random times per day for 28 days. Results: Overall IVR compliance was 55%, with a modal compliance of 60%. Compliance rates did not differ across morning, midday, and evening surveys, but significantly declined over time. The sample was divided into high frequency responders (>70% calls completed, n=35) and low frequency responders (<70%, calls completed n=62). There were no differences between high and low frequency responders on any baseline demographic, tobacco use (nicotine dependence severity), alcohol, or cannabis use characteristics (past 30-day frequency of use). Participants were receptive to IVR-based EMA monitoring and, 16.5% reported purposely decreasing nicotine/tobacco use due to monitoring, while 19.6% reported purposely decreasing cannabis use, which predicted lower cannabis use post-EMA monitoring. Conclusions: Real-time assessment of co-use behavior among young adults does not appear to be impacted by specific demographics or substance use severity (nicotine dependence, heavy drinking). Data suggest some predictive utility of IVR-based EMA monitoring on short-term behavior change. More intensive approaches are needed to improve compliance among young adult cannabis and tobacco co-users.

Associations Between Social Context and Mood During Alcohol Consumption in Young Adult Smokers.

OBJECTIVE: Differences in the subjective effects of alcohol in different social contexts have been well documented, but little research examines affect during drinking in real-world social contexts. This study examined differences by social context in negative affect and positive affect during alcohol consumption. We hypothesized that negative affect and positive affect with drinking would vary as a function of social context (alone or with others). METHOD: A total of 257 young adults (M age = 21.3, 53.3% female) who were enrolled in a longitudinal, observational study assessing risk for smoking completed 7 days of ecological momentary assessment assessing alcohol use, affect, and social context at two time points of the study. Mixed-effects location scale analyses examined effects of being alone versus with others on positive affect and negative affect after drinking and compared with nondrinking times. RESULTS: Positive affect was higher when drinking with others versus alone, and negative affect was higher when drinking alone versus with others. Both negative affect and positive affect variability were higher when participants were drinking alone compared to with others, and negative affect variability was higher at low amounts of alcohol but decreased with increased drinking. CONCLUSIONS: These findings demonstrate that solitary drinking is less consistently reinforcing because of greater and more variable negative affect, as well as more variable positive affect. When drinking with others, increased and less variable positive affect suggests that social drinking may be particularly reinforcing in young adulthood.

Relations of Current and Past Cancer with Severe Outcomes among 104,590 Hospitalized COVID-19 Patients: The COVID EHR Cohort at the University of Wisconsin.

BACKGROUND: There is mixed evidence about the relations of current versus past cancer with severe COVID-19 outcomes and how they vary by patient and cancer characteristics. METHODS: Electronic health record data of 104,590 adult hospitalized patients with COVID-19 were obtained from 21 United States health systems from February 2020 through September 2021. In-hospital mortality and ICU admission were predicted from current and past cancer diagnoses. Moderation by patient characteristics, vaccination status, cancer type, and year of the pandemic was examined. RESULTS: 6.8% of the patients had current (n = 7,141) and 6.5% had past (n = 6,749) cancer diagnoses. Current cancer predicted both severe outcomes but past cancer did not; adjusted odds ratios (aOR) for mortality were 1.58 [95% confidence interval (CI), 1.46-1.70] and 1.04 (95% CI, 0.96-1.13), respectively. Mortality rates decreased over the pandemic but the incremental risk of current cancer persisted, with the increment being larger among younger vs. older patients. Prior COVID-19 vaccination reduced mortality generally and among those with current cancer (aOR, 0.69; 95% CI, 0.53-0.90). CONCLUSIONS: Current cancer, especially among younger patients, posed a substantially increased risk for death and ICU admission among patients with COVID-19; prior COVID-19 vaccination mitigated the risk associated with current cancer. Past history of cancer was not associated with higher risks for severe COVID-19 outcomes for most cancer types. IMPACT: This study clarifies the characteristics that modify the risk associated with cancer on severe COVID-19 outcomes across the first 20 months of the COVID-19 pandemic. See related commentary by Egan et al., p. 3.

Smoking Status, Nicotine Medication, Vaccination, and COVID-19 Hospital Outcomes: Findings from the COVID EHR Cohort at the University of Wisconsin (CEC-UW) Study.

INTRODUCTION: Available evidence is mixed concerning associations between smoking status and COVID-19 clinical outcomes. Effects of nicotine replacement therapy (NRT) and vaccination status on COVID-19 outcomes in smokers are unknown. METHODS: Electronic health record data from 104 590 COVID-19 patients hospitalized February 1, 2020 to September 30, 2021 in 21 U.S. health systems were analyzed to assess associations of smoking status, in-hospital NRT prescription, and vaccination status with in-hospital death and ICU admission. RESULTS: Current (n = 7764) and never smokers (n = 57 454) did not differ on outcomes after adjustment for age, sex, race, ethnicity, insurance, body mass index, and comorbidities. Former (vs never) smokers (n = 33 101) had higher adjusted odds of death (aOR, 1.11; 95% CI, 1.06-1.17) and ICU admission (aOR, 1.07; 95% CI, 1.04-1.11). Among current smokers, NRT prescription was associated with reduced mortality (aOR, 0.64; 95% CI, 0.50-0.82). Vaccination effects were significantly moderated by smoking status; vaccination was more strongly associated with reduced mortality among current (aOR, 0.29; 95% CI, 0.16-0.66) and former smokers (aOR, 0.47; 95% CI, 0.39-0.57) than for never smokers (aOR, 0.67; 95% CI, 0.57, 0.79). Vaccination was associated with reduced ICU admission more strongly among former (aOR, 0.74; 95% CI, 0.66-0.83) than never smokers (aOR, 0.87; 95% CI, 0.79-0.97). CONCLUSIONS: Former but not current smokers hospitalized with COVID-19 are at higher risk for severe outcomes. SARS-CoV-2 vaccination is associated with better hospital outcomes in COVID-19 patients, especially current and former smokers. NRT during COVID-19 hospitalization may reduce mortality for current smokers. IMPLICATIONS: Prior findings regarding associations between smoking and severe COVID-19 disease outcomes have been inconsistent. This large cohort study suggests potential beneficial effects of nicotine replacement therapy on COVID-19 outcomes in current smokers and outsized benefits of SARS-CoV-2 vaccination in current and former smokers. Such findings may influence clinical practice and prevention efforts and motivate additional research that explores mechanisms for these effects.

Introducing a Real-Time Method for Identifying the Predictors of Noncompliance with Event-Based Reporting of Tobacco Use in Ecological Momentary Assessment.

BACKGROUND: Little is known about the factors that bias event-based (i.e., self-initiated) reporting of health behaviors in ecological momentary assessment (EMA) due to the difficulty inherent to tracking failures to self-initiate reports. PURPOSE: To introduce a real-time method for identifying the predictors of noncompliance with event-based reporting. METHODS: N = 410 adults who used both cigarettes and e-cigarettes completed a 1-week EMA protocol that combined random reporting of current contexts with event-based reporting of tobacco use. Each random assessment first asked if participants were currently using tobacco and, if so, the assessment converted into a "randomly captured" event report-indicating failure to self-initiate that report. Multilevel modeling tested predictors of failing to complete random reports and failing to self-initiate event reports. RESULTS: On the person level, male sex, higher average cigarette rate, and higher average cigarette urge each predicted missing random reports. The person-level predictors of failing to self-initiate event reports were older age, higher average cigarette and e-cigarette rates, higher average cigarette urge, and being alone more on average; the moment-level predictors were lower cigarette urge, lower positive affect, alcohol use, and cannabis use. Strikingly, the randomly captured events comprised more of the total EMA reports (28%) than did the self-initiated event reports (24%). These report types were similar across most variables, with some exceptions, such as momentary cannabis use predicting the random capture of tobacco events. CONCLUSIONS: This study demonstrated a method of identifying predictors of noncompliance with event-based reporting of tobacco use and enhancing the real-time capture of events.

This study introduced a real-time method for identifying person- and moment-level predictors of failing to self-initiate tobacco event reports during ecological momentary assessment (EMA), and for capturing a large number of events that would have likely otherwise been missed. The method has implications for behavioral health research more broadly.

Smoking cessation, harm reduction, and biomarkers protocols in the PhenX Toolkit: Tools for standardized data collection.

The use of standard protocols in studies supports consistent data collection, improves data quality, and facilitates cross-study analyses. Funded by the National Institutes of Health, the PhenX (consensus measures for Phenotypes and eXposures) Toolkit is a catalog of recommended measurement protocols that address a wide range of research topics and are suitable for inclusion in a variety of study designs. In 2020, a PhenX Working Group of smoking cessation experts followed a well-established consensus process to identify and recommend measurement protocols suitable for inclusion in smoking cessation and smoking harm reduction studies. The broader scientific community was invited to review and provide feedback on the preliminary recommendation of the Working Group. Fourteen selected protocols for measuring smoking cessation, harm reduction, and biomarkers research associated with smoking cessation were released in the PhenX Toolkit ( https://www.phenxtoolkit.org) in February 2021. These protocols complement existing PhenX Toolkit content related to tobacco regulatory research, substance use and addiction research, and other measures of smoking-related health outcomes. Adopting well-established protocols enables consistent data collection and facilitates comparing and combining data across studies, potentially increasing the scientific impact of individual studies.

Tailored text message and web intervention for smoking cessation in U.S. socioeconomically-disadvantaged young adults: A randomized controlled trial.

The prevalence of cigarette smoking in young adults is higher among those with socioeconomic disadvantage than those without. Low treatment-seeking among young adult smokers is compounded by few efficacious smoking cessation interventions for this group, particularly socioeconomically-disadvantaged young adults (SDYA) who smoke cigarettes. The goal of this study was to test a tailored smoking-cessation intervention for SDYA. 343 SDYA aged 18-30 living in the U.S. (85% female) who smoke cigarettes with access to a smartphone and interest in quitting smoking in the next six months were recruited online in Spring 2020 and randomized to referral to online quit resources (usual care control; n = 171) or a 12-week tailored text message smoking-cessation program with a companion web-based intervention (n = 172). Intent to treat analyses examined associations between study condition, self-reported 30-day point prevalence abstinence (PPA), and confidence to quit smoking at 12 weeks, controlling for potential confounders. Intervention group participants had greater self-reported 30-day PPA at 12-weeks than controls (adjusted relative risk 3.93, 95% CI 2.14-7.24). Among those who continued smoking, the intervention increased confidence to quit (0.81 points, 95% confidence interval 0.08-1.53). Weekly engagement in the intervention predicted greater cessation. A tailored text message intervention for SDYA increased smoking abstinence and confidence to quit at the end-of-treatment. Findings may have been influenced by recruitment at the start of the COVID pandemic but suggest that text messaging is an acceptable and efficacious cessation strategy for SDYA smokers. Future studies should examine the impact on longer-term smoking-cessation and importance of intervention tailoring for SDYA.

Negative Affect and Cigarette Cessation in Dual Users of Cigarettes and Electronic Nicotine Delivery Systems.

Objectives: Dual use of cigarettes and electronic nicotine delivery systems (ENDS) is increasingly common in adult smokers, who often report using ENDS to quit smoking. Elevated negative affect is an established predictor of increased difficulty quitting smoking combustible cigarettes but has not yet been examined in the context of cigarette cessation for dual users. Method: This study examined whether mood-related factors predict cigarette smoking cessation among dual users (N = 364) over 12-months. Self-reported cigarette smoking at 12 months, with abstinence defined as no smoking for the past 7 days, was the primary outcome variable. Logistic regression included baseline levels of depression symptoms (CES-D), anxiety symptoms (MASQ), and negative affect expectancies for smoking, with baseline nicotine dependence for cigarettes (NDSS), motivation to quit, age, race/ethnicity, rate of cigarette smoking at baseline, and ENDS usage at baseline and 12 months as covariates. Interactions between CES-D, MASQ, and negative affect expectancies were examined. We predicted that negative affect, especially for smokers who had high negative affect expectancies for smoking, would be negatively associated with quitting. Results: Contrary to expectations, negative affect constructs did not predict quitting. Baseline nicotine dependence for cigarettes, gender, and race/ethnicity significantly predicted the likelihood of cigarette cessation. Higher rates of ENDS use, higher motivation, and lower negative affect smoking expectancies were significantly correlated with quitting cigarettes. Conclusion: In this non-treatment seeking sample of dual users, negative affect did not predict cigarette cessation over and above nicotine dependence for cigarettes, gender, and race/ethnicity.

A shared-parameter location-scale mixed model to link the responsivity in self-initiated event reports and the event-contingent Ecological Momentary Assessments.

We address the issue of (non-) responsivity of self-initiated assessments in Ecological Momentary Assessment (EMA) or other mobile health (mHealth) studies, where subjects are instructed to self-initiate reports when experiencing defined events, for example, smoking. Since such reports are self-initiated, the frequency and determinants of nonresponse to these event reports is usually unknown, however it may be suspected that nonresponse of such self-initiated reports is not random. In this case, existing methods for missing data may be insufficient in the modeling of these observed self-initiated reports. In certain EMA studies, random prompts, distinct from the self-initiated reports, may be converted to event reports. For example, such a conversion can occur if during a random prompt a subject is assessed about the event (eg, smoking) and it is determined that the subject is engaging in the event at the time of the prompt. Such converted prompts can provide some information about the subject's non-responsivity of event reporting. Furthermore, such non-responsivity can be associated with the primary longitudinal EMA outcome (eg, mood) in which case a joint modeling of the non-responsivity and the mood outcome is possible. Here, we propose a shared-parameter location-scale model to link the primary outcome model for mood and a model for subjects' non-responsivity by shared random effects which characterize a subject's mood level, mood change pattern, and mood variability. Via simulations and real data analysis, our proposed model is shown to be more informative, have better coverage of parameters, and provide better fit to the data than more conventional models.

The Mood Boost from Tobacco Cigarettes is More Erratic with the Additions of Cannabis and Alcohol.

INTRODUCTION: A large body of literature indicates that nicotine results in an acute mood "boost," including increased positive affect and decreased negative affect. Young adults frequently engage in polysubstance use of cigarettes with cannabis and alcohol-a trend that is likely to accelerate with the expanding legalization of cannabis. However, little is known about whether polysubstance use, defined here as combustible tobacco cigarette use within the same hour as cannabis and alcohol, is associated with changes in the nicotine mood boost. The present study aimed to address this gap. METHODS: Young adults (N = 202, 52% female, mean age = 21 years at time 1) provided ecological moments assessment (EMA) reports of cigarette use over two 7-day bursts spaced 1 year apart. In each report, participants rated mood levels before and after smoking, and indicated cannabis and alcohol use. Mixed-effects location-scale modeling simultaneously tested changes in mood levels and variability related to smoking events with cannabis and/or alcohol compared with smoking-only events. RESULTS: From before to after smoking, positive affect increased and negative affect decreased, on average. Overall, the additions of cannabis and/or alcohol had nonsignificant associations with these mean changes. However, polysubstance use, as well as cigarette-cannabis co-use, were each associated with significantly greater within-person variability in the positive and negative affect changes related to smoking. CONCLUSIONS: The mood benefits associated with smoking were more erratic in the contexts of polysubstance use and cigarette-cannabis co-use. Potential implications for young adults' long-term nicotine use trajectories are discussed. IMPLICATIONS: Among young adults who smoke cigarettes, the mood "boost" from smoking may be more erratic-which is to say, more likely to be either amplified or attenuated-with the additions of cannabis and alcohol together, or cannabis alone. On occasions when young adults seek out cannabis and alcohol to enhance their smoking mood boost, but instead experience a dampening effect, they might consume more nicotine, contributing over time to greater dependence. Future investigation is warranted, with particular attention to nicotine-cannabis co-use.

Relations among cigarette dependence, e-cigarette dependence, and key dependence criteria among dual users of combustible and e-cigarettes.

Objective: The aim of this study was to examine dependence on combustible and e-cigarettes among users of both products (dual users), which may provide important insights into long-term use patterns. Method: Dual users (smoking daily for 3 months, using e-cigarettes at least once/week for the past month; N = 256; 45% women, 71% White, M age 39.0 years) not interested in quitting either product participated in a longitudinal, 2-year, observational study. At baseline, participants completed measures of combustible and e-cigarette dependence (Fagerström Test of Cigarette Dependence [FTCD], e-FTCD, Wisconsin Inventory of Smoking Dependence Motives [WISDM], e-WISDM, Penn State Cigarette Dependence Index, and Penn State E-Cigarette Dependence Index) and carried a study smartphone for 2 weeks to record cigarette and e-cigarette use events. Results: Most measures of dependence were product specific (e.g., FTCD and e-FTCD were not correlated, r = -0.003) and predicted product-specific outcomes (e.g., long-term use of that product). However, individuals used the two products for some of the same secondary dependence motives (e.g., weight control, cognitive and affective enhancement). These secondary, or instrumental, motives predicted use of both products at 1 year. Which product was used first in the morning was strongly related to product dependence scores and likelihood of continued product use at 1 year. Conclusions: Among dual users of combustible and e-cigarettes, measures of e-cigarette and cigarette dependence tended to be unrelated to one another, but dual users tended to use both products for the same instrumental motives. Which product is used first in the morning may serve as a valuable measure of relative dependence on the two products. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Preferred flavors and tobacco use patterns in adult dual users of cigarettes and ENDS.

OBJECTIVES: This study examined how adult dual users of cigarettes and electronic nicotine delivery system (ENDS) flavor preference varied by demographics, tobacco history, motives, and expectancies for ENDS, and how ENDS flavor preference was associated with changes in cigarette and ENDS use over 12 months. METHODS: Data come from the baseline and 12-month waves of an observational study of adult dual cigarette and ENDS users (N = 406). Flavor preferences were grouped into 4 categories: tobacco (12.6%), menthol/mint (34.7%), sweet (44.8%), and other (7.9%). RESULTS: Users of sweet-flavored ENDS were significantly younger than those who used tobacco- or menthol flavors. Black dual users were significantly more likely than other racial groups to use menthol and less likely to use sweet flavors. Dual users who preferred sweet flavors smoked cigarettes on fewer days than those who preferred tobacco and menthol flavors, were less cigarette dependent, more strongly endorsed boredom reduction expectancies and motives related to taste and sensory experience and were more likely to stop smoking by 12 months. CONCLUSIONS: Dual users of cigarettes and ENDS who preferred sweet flavored ENDS differed in demographics, tobacco history, motives, expectancies, and smoking changes. Findings have implications for interventions and regulations.

Analysis of multivariate longitudinal substance use outcomes using multivariate mixed cumulative logit model.

BACKGROUND: Longitudinal assessments of usage are often conducted for multiple substances (e.g., cigarettes, alcohol and marijuana) and research interests are often focused on the inter-substance association. We propose a multivariate longitudinal modeling approach for jointly analyzing the ordinal multivariate substance use data. METHODS: We describe how the binary random slope logistic regression model can be extended to the multi-category ordinal outcomes. We also describe how the proportional odds assumption can be relaxed by allowing differential covariate effects on different cumulative logits for multiple outcomes. Data are analyzed from a P01 study that evaluates the usage levels of cigarettes, alcohol and marijuana repeatedly across 8 measurement waves during 7 consecutive years. RESULTS: 1263 subjects participated in the study with informed consent, among whom 56.6% are females. Males and females show significant differences in terms of the time trend for substance use. Specifically, males showed steeper trends on cigarette and marijuana use over time compared to females, while less so for alcohol. For all three substances, age effects appear to be different for different cumulative logits, indicating the violation of proportional odds assumption. CONCLUSIONS: The multivariate mixed cumulative logit model offers the most flexibility and allows one to examine the inter-substance association when proportional odds assumption is violated.

Balancing Consideration of the Risks and Benefits of E-Cigarettes.

The topic of e-cigarettes is controversial. Opponents focus on e-cigarettes' risks for young people, while supporters emphasize the potential for e-cigarettes to assist smokers in quitting smoking. Most US health organizations, media coverage, and policymakers have focused primarily on risks to youths. Because of their messaging, much of the public-including most smokers-now consider e-cigarette use as dangerous as or more dangerous than smoking. By contrast, the National Academies of Science, Engineering, and Medicine concluded that e-cigarette use is likely far less hazardous than smoking. Policies intended to reduce adolescent vaping may also reduce adult smokers' use of e-cigarettes in quit attempts. Because evidence indicates that e-cigarette use can increase the odds of quitting smoking, many scientists, including this essay's authors, encourage the health community, media, and policymakers to more carefully weigh vaping's potential to reduce adult smoking-attributable mortality. We review the health risks of e-cigarette use, the likelihood that vaping increases smoking cessation, concerns about youth vaping, and the need to balance valid concerns about risks to youths with the potential benefits of increasing adult smoking cessation.

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.

Risk factors for alcohol, marijuana, and cigarette polysubstance use during adolescence and young adulthood: A 7-year longitudinal study of youth at high risk for smoking escalation.

INTRODUCTION: Alcohol, nicotine, and marijuana are the three most widely used substances among adolescents and young adults, with co-use of multiple substances being common. Few longitudinal studies have examined risk factors of alcohol, marijuana, and nicotine poly-substance use. We examined frequency of alcohol, marijuana, and cigarette poly-substance use over time and how key risk factors contribute to this substance use during adolescence and young adulthood. METHODS: Participants (N = 1263 9th and 10th graders) were oversampled for ever-smoking a cigarette at baseline from 16 Chicago-area high schools between 2004 and 2006. Many participants progressed to heavier cigarette use, as well as alcohol and marijuana use over time. Participants completed questionnaires assessing substance use and psychosocial factors at baseline, 6-, 15-, 24-, 33-months, and 5-, 6-, and 7-years. RESULTS: Longitudinal multi-level models demonstrated that at baseline and over time, more depression symptoms, more anxiety symptoms, negative mood regulation expectancies, and lower grade point average (GPA) were each associated with more poly-substance use over time. In addition, there were a number of interaction effects of gender (e.g., depression was related to substance use in males) and developmental stage moderated these relationships. CONCLUSIONS: Depression, anxiety, negative mood regulation expectancies, and GPA all significantly influence both initial and longitudinal levels of substance use across adolescence and young adulthood. Our findings underscore the importance of identifying and treating youth with depression and anxiety symptoms, as well as providing resources early for those struggling in school in order to help with substance use prevention and intervention efforts.