The Impact of Three Alternate Nicotine-Delivery Products on Combusted Cigarette Use: A Randomized Controlled Trial.

INTRODUCTION: Smoking cessation is a critical public health goal. This study examined the ability of e-cigarettes and very low nicotine cigarettes (VLNCs) to serve as cigarette substitutes and whether a substitution was supported by steady-state nicotine from a nicotine patch. AIMS AND METHODS: This mixed design experiment with study product (between-subjects) and patch (within-subjects) factors recruited adults smoking cigarettes daily and not motivated to quit (N = 160). Participants were randomized to 4 weeks of: (1) VLNCs; (2) e-cigarettes; or (3) no product. During two switch weeks, one with an active nicotine patch and one with a placebo patch (in a double-blind and counterbalanced fashion), participants were told to not smoke their usual cigarettes. RESULTS: During the switch weeks, participants in the VLNC (M = 2.88, SD = .65) and e-cigarette (M = 3.20, SD = .63) groups smoked fewer of their own cigarettes per day than did no product group participants who continued to smoke their own cigarettes (M = 5.48, SD = .63); the VLNC and e-cigarette groups did not differ. There was no main effect of patch on mean usual brand cigarettes smoked per day (P = .09), nor was there a product × patch interaction (P = .51). There was a product × age interaction (P = .03); smokers aged 60-74 smoked more of their own cigarettes if they were randomized to no product group. CONCLUSIONS: VLNCs and e-cigarettes appear to reduce usual brand cigarettes smoked per day to a similar degree, regardless of patch condition. Behavioral factors, in addition to nicotine dependence, play an important role in sustaining smoking behavior and need to be addressed in smoking cessation treatment. IMPLICATIONS: This study found that behavioral substitutes for cigarettes, whether or not they delivered nicotine, reduced the number of usual brand cigarettes smoked. Specifically, both e-cigarettes delivering nicotine and VLNCs equally reduce usual brand cigarettes smoked among adults who smoke daily and do not want to quit.

Subtypes of Dual Users of Combustible and Electronic Cigarettes: Longitudinal Changes in Product Use and Dependence Symptomatology.

INTRODUCTION: Cross-sectional surveys found behavioral heterogeneity among dual users of combustible and electronic cigarettes. Yet, prior classification did not reflect dynamic interactions between cigarette and e-cigarette consumption, which may reveal changes in product-specific dependence. The contexts of dual use that could inform intervention were also understudied. METHODS: This study conducted secondary analysis on 13 waves of data from 227 dual users who participated in a 2-year observational study. The k-means method for joint trajectories of cigarette and e-cigarette consumption was adopted to identify the subtypes of dual users. The time-varying effect model was used to characterize the subtype-specific trajectories of cigarette and e-cigarette dependence. The subtypes were also compared in terms of use contexts. RESULTS: The four clusters were identified: light dual users, predominant vapers, heavy dual users, and predominant smokers. Although heavy dual users and predominant smokers both smoked heavily at baseline, by maintaining vaping at the weekly to daily level the heavy dual users were able to considerably reduce cigarette use. Yet, the heavy dual users' drop in cigarette dependence was not as dramatic as their drop in cigarette consumption. Predominant vapers appeared to engage in substitution, as they decreased their smoking and increased their e-cigarette dependence. They were also more likely to live in environments with smoking restrictions and report that their use of e-cigarettes reduced cigarette craving and smoking frequency. CONCLUSIONS: Environmental constraints can drive substitution behavior and the substitution behavior is able to be sustained if people find the substitute to be effective. IMPLICATIONS: This study characterizes subtypes of dual users based on the dynamic interactions between cigarette use and e-cigarette use as well as product-specific trajectories of dependence. The subtypes differ in not only sociodemographic characteristics but also contexts of cigarette and e-cigarette use. Higher motivation to use e-cigarettes to quit smoking and less permissive environment for smoking may promote substitution of cigarettes by e-cigarettes.

Associations of Demographics, Dependence, and Biomarkers With Transitions in Tobacco Product Use in a Cohort of Cigarette Users and Dual Users of Cigarettes and E-cigarettes.

INTRODUCTION: It is uncertain whether e-cigarettes facilitate smoking cessation in the real world. We aimed to understand whether and how transitions among cigarette, e-cigarette, and dual use are associated with sociodemographics, dependence measures, and biomarkers. AIMS AND METHODS: We followed 380 adult daily cigarette users and dual users every 2 months for up to 2 years. We estimated transition rates between noncurrent, cigarette-only, e-cigarette-only, and dual use states using a multistate transition model. We estimated univariable hazard ratios (HR) for demographics, dependence measures for cigarettes and e-cigarettes, biomarkers, spousal or partner behaviors, and other measures. RESULTS: We estimated that participants transitioned from cigarette-only to e-cigarette-only through a period of dual use. Dual users ceased smoking (transitioning to e-cigarette-only use) at a greater rate than cigarette-only users did (HR 2.44, 95% CI: 1.49, 4.02). However, of the 60% of dual users estimated to transition to single product use in 1 year, 83% would transition to cigarette-only use and only 17% to e-cigarette-only use. E-cigarette dependence measures were generally associated with reduced e-cigarette cessation rather than enhanced cigarette cessation. E-cigarette users motivated by harm or toxicity reduction or because of restrictions on where or when they could smoke had reduced rates of smoking relapse. Cigarette dependence and spousal smoking were barriers to cigarette cessation for dual users, while using e-cigarettes first in the morning, motivation to quit smoking, and sensory, social, and emotional enjoyment of e-cigarettes (secondary dependence motives) were facilitators of smoking cessation among dual users. CONCLUSIONS: Tobacco control policy and interventions may be informed by the barriers and facilitators of product transitions. IMPLICATIONS: Although e-cigarettes have the potential to promote smoking cessation, their real-world impact is uncertain. In this cohort, dual users were more likely to quit smoking than cigarette-only users, but the overall impact was small because most dual users returned to cigarette-only use. Moreover, e-cigarette dependence promoted continued dual use rather than smoking cessation. Yet, high motivation to quit smoking and the sensory, social, and emotional enjoyment of e-cigarettes facilitated smoking cessation in dual users. Better understanding the barriers and facilitators of transitions can help to develop regulations and interventions that lead to more effective use of e-cigarettes for smoking cessation.

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.

Reporting Treatment Fidelity in Behavioral Tobacco Treatment Clinical Trials: Scoping Review and Measurement Recommendations.

INTRODUCTION: Adoption of rigorous standards for reporting treatment fidelity is essential for advancing discovery, validation, and implementation of behavioral treatments. Whereas the NIH Behavior Change Consortium (BCC) developed an assessment tool to assess the quality of reporting and monitoring of treatment fidelity across health behavior change interventions, it has not yet been applied specifically to treatment fidelity in behavioral tobacco treatment trials. AIMS AND METHODS: We conducted a scoping review of peer-reviewed, clinical trials of behavioral adult tobacco treatment interventions published in English between 2006 and 2018. Using the BCC treatment fidelity checklist, articles were coded for the presence or absence of various treatment fidelity strategies within each of 5 domains: Design, Training, Delivery, Receipt, and Enactment. Eligible articles (N = 755) were coded by two independent coders. RESULTS: The proportion of reporting strategies varied within the fidelity domains, ranging from 5.2% to 96.3% in Design, 1.9% to 24.9% in Training, 2.6% to 32.3% in Delivery, 5.2% to 44.3% in Receipt, and 6.7% to 43.2% in Enactment. The mean proportion of adherence to treatment fidelity strategies within each domain was: Design (68%), Training (14%), Delivery (15%), Receipt (16%), and Enactment (25%). Only 11 studies achieved ≥80% reporting across >1 fidelity domain. There was no evidence for improvement in fidelity reporting across the 13-year time frame from the initial BCC publication to the present. CONCLUSIONS: These findings illustrate the lack of consistency in fidelity reporting in tobacco treatment trials and underscore the challenges faced in evaluating rigor and reproducibility, as well as interpretation and dissemination of findings. Recommendations are made for improving fidelity reporting in tobacco treatment trials. IMPLICATIONS: The SRNT Treatment Research Network sponsored a scoping review to summarize the current state of reporting treatment fidelity and make recommendations for best practices in reporting fidelity in tobacco treatment trials. The review identified a lack of consistency in fidelity reporting, illustrating the challenges faced in evaluating rigor, and reproducibility, as well as interpretation and dissemination of findings.

A new statistical model for longitudinal ecological momentary assessment data on dual use of electronic and combustible cigarettes.

Background: Existing studies of dual use of electronic and combustible cigarettes either collected longitudinal data with long gaps in between waves or conducted ecological momentary assessment (EMA) over a short period of time. In recent years, the measurement burst design that embeds an EMA protocol in each wave assessment of a traditional longitudinal study has become more popular and yet conventional generalized linear mixed models (GLMM) have important limitations for handling data from this design.Objectives: This study proposed a new statistical method to analyze data from the measurement burst design.Methods: This new statistical method was designed to model the short-term (within-wave) as well as long-term (between-wave) changes and was validated by a simulation study. Secondary analysis was conducted to analyze data from 205 dual users (52% male) and 146 exclusive smokers (50% male) who participated in a recent study using the measurement burst design.Results: The simulation study shows that the proposed method can handle the gap between waves well and is also robust to nonlinear changes across waves. Although no short-term change in smoking was found, dual users reported a long-term reduction in cigarette use that was more rapid compared to exclusive smokers (߈=-0.0127,p=.0167). Vaping more was associated with smoking less (߈=-0.0058,p=.0054).Conclusion: The proposed method is highly applicable as it can be easily implemented by substance use researchers and the results can be straightforwardly interpreted. The results suggest that e-cigarette use may play a role in promoting a long-term reduction in smoking among dual users.

Validating E-Cigarette Dependence Scales Based on Dynamic Patterns of Vaping Behaviors.

INTRODUCTION: Existing e-cigarette dependence scales are mainly validated based on retrospective overall consumption or perception. Further, given that the majority of adult e-cigarette users also use combustible cigarettes, it is important to determine whether e-cigarette dependence scales capture the product-specific dependence. This study fills in the current knowledge gaps by validating e-cigarette dependence scales using novel indices of dynamic patterns of e-cigarette use behaviors and examining the association between dynamic patterns of smoking and e-cigarette dependence among dual users. METHODS: Secondary analysis was conducted on the 2-week ecological momentary assessment data from 116 dual users. The Smoothly Clipped Absolute Deviation penalty (SCAD) was adopted to select important indices for dynamic patterns of consumption or craving and estimate their associations with e-cigarette dependence scales. RESULTS: The fitted linear regression models support the hypothesis that higher e-cigarette dependence is associated with higher levels of e-cigarette consumption and craving as well as lower instability of e-cigarette consumption. Controlling for dynamic patterns of vaping, dual users with lower e-cigarette dependence tend to report higher day-to-day dramatic changes in combustible cigarette consumption but not higher average levels of smoking. CONCLUSIONS: We found that more stable use patterns are related to higher levels of dependence, which has been demonstrated in combustible cigarettes and we have now illustrated in e-cigarettes. Furthermore, the e-cigarette dependence scales may capture the product-specific average consumption but not product-specific instability of consumption. IMPLICATIONS: This study provides empirical support for three e-cigarette dependence measures: PS-ECDI, e-FTCD, and e-WISDM, based on dynamic patterns of e-cigarette consumption and craving revealed by EMA data that have great ecological validity. This is the first study that introduces novel indices of dynamic patterns and demonstrates their potential applications in vaping research.

Changes in Use Patterns Over 1 Year Among Smokers and Dual Users of Combustible and Electronic Cigarettes.

BACKGROUND: Dual use of combustible and electronic cigarettes (e-cigarettes) is a growing use pattern; more than half of e-cigarette users are dual users. However, little is known regarding the course of dual use; for example, the likelihood of discontinuation of either combustible or e-cigarettes or both. METHODS: Adult daily smokers and dual users (daily smokers who also vaped at least once per week) who did not intend to quit use of either product in the next 30 days participated in a longitudinal, observational study (N = 322, 51.2% women, 62.7% white, mean age = 42.27 [SD = 14.05]). At baseline, participants completed demographics and smoking and vaping history assessments. They also reported daily cigarette and e-cigarette use via timeline follow-back assessment and provided a breath sample for carbon monoxide assay at 4-month intervals for 1 year. RESULTS: Of those who completed the year 1 follow-up, 1.9% baseline smokers and 8.0% dual users achieved biochemically confirmed seven-day point-prevalence abstinence from combustible cigarettes (χ2 = 4.57, p = .03). Of initial dual users, by 1 year 43.9% were smoking only, 48.8% continued dual use, 5.9% were vaping only, and 1.4% abstained from both products. Among baseline smokers, 92.3% continued as exclusive smokers. Baseline dual users who continued e-cigarette use were more likely to be white and report higher baseline e-cigarette dependence. CONCLUSIONS: In this community sample, the majority of dual users transitioned to exclusive smoking. A higher percentage of dual users quit smoking than smokers, but attrition and baseline differences between the groups compromise strong conclusions. Sustained e-cigarette use was related to baseline e-cigarette dependence. IMPLICATIONS: This research suggests that dual use of combustible and e-cigarettes is not a sustained pattern for the majority of dual users, but it is more likely to be a continued pattern if the user is more dependent on e-cigarettes. There was evidence that dual users were more likely to quit smoking than exclusive smokers, but this may be due to factors other than their dual use.

E-cigarette Dependence Measures in Dual Users: Reliability and Relations With Dependence Criteria and E-cigarette Cessation.

BACKGROUND: Electronic cigarettes (e-cigarettes) have drastically changed the nicotine and tobacco product landscape. However, their potential public health impact is still unclear. A reliable and valid measure of e-cigarette dependence would likely advance assessment and prognostication of the public health impact of e-cigarettes. The aim of this research was to examine the internal consistency, structure, and validity of three e-cigarette dependence scales. METHODS: Adult dual users (smokers who also vape, N = 256) enrolled in an observational cohort study (45.1% women, 70.7% white). At baseline, participants completed the e-cigarette Fagerström Test of Cigarette Dependence (e-FTCD), the e-cigarette Wisconsin Inventory of Smoking Dependence Motives (e-WISDM), and the Penn State Electronic Cigarette Dependence Index (PS-ECDI). All participants provided a urine sample for cotinine analysis and reported e-cigarette use at 1 year. RESULTS: The e-WISDM subscales had the highest internal consistency (α = .81-.96), then the PS-ECDI (α = .74) and e-FTCD (α = .51). A single-factor structure for the e-FTCD and an 11-factor structure for the e-WISDM were supported, but the PS-ECDI did not have a single-factor structure. All three e-cigarette dependence scales were highly correlated with validation criteria including continued e-cigarette use at 1 year, but not with e-liquid nicotine concentration or cotinine. CONCLUSIONS: The e-WISDM and PS-ECDI had stronger internal consistency than did the e-FTCD, despite the e-FTCD's single-factor structure, but all 3 measures appear to be valid measures of e-cigarette dependence as suggested by their significant relations with self-perceived addiction, heavy use, early use after overnight deprivation, and continued use over time. IMPLICATIONS: This research provides empirical support for three e-cigarette dependence measures: the e-FTCD, the PS-ECDI, and the e-WISDM among dual users of e-cigarettes and combustible cigarettes. The PS-ECDI and e-WISDM are more reliable, but all three measures were strongly correlated with key dependence constructs such as heavy use and continued use over time.

Predictors of Smoking Cessation Attempts and Success Following Motivation-Phase Interventions Among People Initially Unwilling to Quit Smoking.

INTRODUCTION: Most people who smoke cigarettes are not willing (ie, not ready) to make a quit attempt (QA) at any given time. Unfortunately, interventions intended to increase QAs and the success of QAs are only modestly effective. Identifying processes leading to QAs and quitting success could guide intervention development. AIMS AND METHODS: This is a secondary analysis of a randomized factorial trial of 6 weeks of motivation-phase interventions among primary care patients (N = 517) who were initially unwilling to quit but were willing to reduce their smoking. Using logistic regression, we controlled for treatment condition and tested whether baseline or change in smoking-related constructs after 6 weeks of treatment predicted (1) making an at least 24 h QA between weeks 6 and 26 and (2) quitting success at week 26 (7-day point-prevalence abstinence among those who made a QA). Predictors included cigarettes/day, time to first cigarette, motivation to quit, quitting self-efficacy, anticipated urges to smoke if quit, positive affect, negative affect, and time spent around others who smoke. RESULTS: In multivariable models that included all smoking-related constructs, changes in the following variables predicted initiating a QA above and beyond other variables: greater baseline time to first cigarette (odds ratio [OR] = 1.60), increases in time to first cigarette (OR = 1.27), and increases in quitting self-efficacy (OR = 1.14). Increased motivation to quit predicted conversion of a QA into quitting success at 26 weeks (OR = 1.36). CONCLUSION: Predictors of making a QA differed from predictors of quitting success. Predictors of QAs and success could each serve as important treatment targets of motivation-phase interventions. IMPLICATIONS: Motivation-phase interventions for people initially unwilling to quit smoking cigarettes may be improved by striving to increase their (1) time to first cigarette and quitting self-efficacy to promote QAs and (2) motivation to quit to promote quit success. Future experimental tests of such interventions are needed to identify causal determinants of QAs and quitting success.

Helping women veterans quit smoking: a qualitative analysis of successful and unsuccessful attempts.

BACKGROUND: Tobacco use is the number one cause of death and disability of women in the United States, and our women Veteran population is disproportionately affected. Despite revisions to the Veterans Affairs' approach to smoking cessation, women continue to smoke at equal or higher rates than men, are prescribed cessation medications less frequently, and are less likely to quit. In this qualitative pilot study, individual interviews with women Veterans revealed their experiences with smoking cessation attempts. METHODS: The lead author conducted semi-structured interviews with 14 women Veterans who were either current or former smokers. Participants gave a narrative account of recent quit smoking attempts. Inductive thematic analysis explored the underlying themes. RESULTS: Four main themes emerged as important: health and well-being, smoking as an addiction, optimism, and resilience. Health and well-being encompassed physical health, mental health, and financial stability. Women often felt that stability in these key areas made a successful attempt possible. Women with successful quit attempts were more likely to consider tobacco use as an addiction, as well as expressed optimism about their quit attempts. Women with successful quit attempts also demonstrated more resilience to lapses and relapses. CONCLUSIONS: Women Veterans' quit smoking attempts demonstrate four main themes: baseline health and wellbeing, acknowledging smoking as an addiction, the participant's optimism towards quitting, and resilience. Patterns were observed within themes with respect to whether the woman was currently quit or had experienced a prolonged quit attempt in the past. Further research is needed to help women Veterans quit smoking.

Dual Users Compared to Smokers: Demographics, Dependence, and Biomarkers.

INTRODUCTION: The availability of electronic cigarettes (e-cigarettes) has profoundly changed the tobacco product landscape. In the United States, almost 6 million adults use both combustible and e-cigarettes (ie, dual users). The goal of this study was to understand how smokers and dual users differ in terms of demographics, cigarette dependence, and exposure to carcinogens. METHODS: An observational cohort (smokers, n = 166, ≥5 cigarettes/day for 6 months and no e-cigarette use in 3 months; dual users, n = 256, smoked daily for 3 months and used e-cigarettes at least once/week for the past 3 months) completed baseline assessments of demographics, tobacco use, and dependence. They also provided breath samples for carbon monoxide (CO) assay and urine samples for cotinine, 3-hydroxycotinine, and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) assays. RESULTS: Compared to smokers, dual users (mean e-cigarette use = 5.5 days/week [SD = 1.9]) were significantly younger and more likely to be white, have more education, report a history of psychiatric co-morbidity, and smoke fewer cigarettes per day. There were no differences in CO, cotinine, or 3-hydroxycotinine levels; however, dual users had significantly lower levels of NNAL than did smokers. Most smokers and dual users had no plans to quit smoking within the next year; 91% of dual users planned to continue using e-cigarettes for at least the next year. CONCLUSIONS: In this community sample, dual users are supplementing their smoking with e-cigarette use. Dual users, versus smokers, smoked fewer cigarettes per day and delayed their first cigarette of the day, but did not differ in quitting intentions. IMPLICATIONS: This comparison of a community sample of established dual users and exclusive smokers addresses key questions of dependence and health risks of dual use in real-world settings. Dual users were more likely to be white, younger, have more than a high school education and have a psychiatric history. Dual users also smoked significantly fewer cigarettes and had lower levels of NNAL (a carcinogen), but they did not differ from exclusive smokers in CO or cotinine levels, suggesting that they supplemented their nicotine intake via e-cigarettes.

A Randomized Controlled Trial of an Optimized Smoking Treatment Delivered in Primary Care.

Background: The effectiveness of smoking cessation treatment is limited in real-world use, perhaps because we have not selected the components of such treatments optimally nor have treatments typically been developed for and evaluated in real-world clinical settings. Purpose: To validate an optimized smoking cessation treatment package that comprises intervention components identified as effective in factorial screening experiments conducted as per the Multiphase Optimization Strategy (MOST). Methods: Adult smokers motivated to quit were recruited from primary care clinics (N = 623). Participants were randomized to receive either recommended usual care (R-UC; 10 min of in-person counseling, 8 weeks of nicotine patch, and referral to quitline services) or abstinence-optimized treatment (A-OT; 3 weeks of prequit mini-lozenges, 26 weeks of nicotine patch + mini-lozenges, three in-person and eight phone counseling sessions, and 7-11 automated calls to prompt medication use). The key outcomes were self-reported and biochemically confirmed (carbon monoxide, CO <6 ppm) 7-day point-prevalence abstinence. Results: A-OT participants had significantly higher self-reported abstinence rates than R-UC participants at 4, 8, 16, and 26 weeks (ORs: 1.91-3.05; p <. 001). The biochemically confirmed 26-week abstinence rates were lower than the self-reported 26-week rates, but revealed a similar treatment effect size (OR = 2.94, p < .001). There was no moderation of treatment effects on 26-week abstinence by demographic, psychiatric, or nicotine dependence variables. A-OT had an incremental cost-effectiveness ratio for 26-week CO-confirmed abstinence of $7,800. Conclusions: A smoking cessation treatment that is optimized via MOST development meaningfully enhances cessation rates beyond R-UC smoking treatment in smokers seen in primary care. Clinical Trial Registration: NCT02301403.

Cessation-related information, motivation, and behavioral skills in smokers living with HIV.

There is a need for new, targeted smoking cessation interventions for smokers living with HIV. The Information-Motivation-Behavioral Skills (IMB) model has been applied effectively to HIV-related health behaviors and was used in this qualitative study to elicit factors that could lead to the development of innovative and successful cessation interventions for this population. Twenty individuals who smoked from two clinics providing care to people living with HIV participated in open-ended interviews, responding to questions covering the domains of the IMB model, as applied to smokers living with HIV. Participants were enrolled from a larger survey cohort to recruit into groups based on the impact of HIV diagnosis on smoking as well as attempting to enroll a mix of demographics characteristics. Interviews were recorded, transcribed, coded and thematically analyzed using a grounded theory qualitative approach. Interviews continued until thematic saturation was reached. Major themes included: Presence of knowledge deficits regarding HIV-specific health risks of smoking; use of smoking for emotional regulation, where many reported close contacts who smoke and concern with the effect of cessation on their social networks; Use of smoking cessation aids or a telephone-based wellness intervention were acceptable to most. Providing HIV-specific information in cessation advice is of the utmost importance for clinicians caring for smokers living with HIV, as this theme was noted consistently as a potential motivator to quit. Innovative and effective interventions must account for the social aspect of smoking and address other methods of emotional regulation in this population.

Identifying Differences in Rates of Invitation to Participate in Tobacco Treatment in Primary Care.

INTRODUCTION: The progress achieved in reducing tobacco use has not been consistent across groups of smokers, and health systems are inconsistently implementing best practice guidelines. Guideline implementation could be associated with improved treatment invitation rates. AIMS: To evaluate differences in tobacco treatment invitation rates based on patient characteristics in primary care clinics implementing best practice guidelines. METHODS: A secondary analysis of patients presenting to 11 primary care clinics from 2 Wisconsin health systems from June 2010 to February 2013. The main outcome was whether patients received an invitation to participate in tobacco treatment. Invitation rates were examined by sex, age group (≤ 24 years, 25-44, 45-64, ≥ 64), race (white, black, other), insurance status (private, Medicare, Medicaid, none), and visit diagnosis ("high-risk" [cardiovascular and pulmonary disease, malignancy, pregnancy] vs "low-risk" [all other ICD-9 categories]). Moderation effects of health systems also were examined. RESULTS: Of the 95,471 patients seen, 84,668 (89%) were screened for smoking. Among the 15,193 smokers, 10,242 (67%) were invited to participate. Invited patients were older, white or black, and carried low-risk diagnoses. Invitation rates and patient-level differences varied between the health systems. CONCLUSIONS: Variable treatment invitation rates and health system differences remain evident in the primary care setting employing robust clinical practice guideline recommendations.

Understanding the motivational mechanisms for smoking and vaping among dual users and exclusive smokers.

BACKGROUND: Understanding the motivational processes that influence e-cigarette use in a laboratory setting may help elucidate mechanisms that support long-term ecigarette use, which could have significant clinical and public health consequences. METHODS: Secondary analyses were conducted on data from exclusive smokers (N=47) and dual users (N=88) who underwent a laboratory ad lib use session. Participants were given 10minutes to smoke (exclusive smokers) or vape (dual users) as much as they wanted. Withdrawal was assessed pre- and post-use. Smoking and vaping behavior was coded from session videos. Person-level predictors included cigarette/ecigarette craving-relief expectancies, demographics, and cigarette/e-cigarette use and dependence. Smoking and vaping status was assessed at Year 1 using self-reported 30-day point prevalence. Data were analyzed using general linear models and logistic regressions. RESULTS: Both groups reported reductions in withdrawal after product use, including cigarette craving. Baseline e-cigarette craving-relief expectancies, pre-session ecigarette craving, heaviness of e-cigarette use, and relative e-cigarette dependence were significant univariate predictors of continued vaping in dual users at Year 1 (ORs>1.04, ps<.05). Dual users and exclusive smokers did not differ on use behavior (i.e., average number of puffs, ps>.16). CONCLUSIONS: E-cigarette use alleviated withdrawal, including cigarette and e-cigarette craving, in dual users. Laboratory use behavior did not differ between dual users using e-cigarettes and exclusive smokers using cigarettes. Greater e-cigarette craving-relief expectancies, e-cigarette craving, heaviness of e-cigarette use, and morning product use pattern ('relative dependence') may reflect mechanisms that sustain e-cigarette use.

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.

Dependence motives and use contexts that predicted smoking cessation and vaping cessation: A two-year longitudinal study with 13 waves.

INTRODUCTION: Prior studies examining the impact of e-cigarette use, dependence, cessation motivation/goals, and environmental restriction on smoking cessation were based on cross-sectional or shorter-term longitudinal data with binary outcomes. There is also a critical knowledge gap in corresponding impact on vaping cessation. This study aims to fill in these gaps by investigating these factors' effects on speed of progression to smoking and vaping cessation. METHODS: This study conducted secondary analysis of data from 13 waves of assessment of adult cigarette users in Wisconsin from October 2015 through July 2019. Cox regression was employed to examine baseline predictors' effects on speed of progression to smoking cessation (past-month abstinence) among 405 exclusive combustible cigarette users and dual users of combustible and electronic cigarettes, as well as progression to vaping cession among 178 dual users. RESULTS: Dual use of e-cigarettes with cigarettes, lower primary dependence motives of smoking, higher secondary dependence motives of smoking, higher motivation to quit smoking, more ambitious future goals to quit smoking, and more restrictive environment for smoking all contributed to quicker progression to smoking cessation. Dual users with higher secondary dependence motives of smoking or with lower primary dependence motives of vaping progressed faster to vaping cessation. CONCLUSIONS: The findings support that nicotine dependence is product-specific with two distinct constructs: the primary dependence motives are associated with more difficulty to quit, whereas the secondary dependence motives have the opposite effect. Dual users with strong instrumental reasons for smoking may not find e-cigarettes as an effective substitute for cigarettes.

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.