Reengagement for Long-Term Smoking-Cessation In Military Personnel, Retirees, Family Members (TRICARE): A Randomized Trial.

INTRODUCTION: We sought to determine what type of treatment reengagement after smoking relapse would increase long-term cessation. AIMS AND METHODS: Participants were military personnel, retirees, and family members (TRICARE beneficiaries) recruited across the United States from August 2015 through June 2020. At baseline, consented participants (n = 614) received a validated, four-session, telephonic tobacco-cessation intervention with free nicotine replacement therapy. At the 3-month follow-up, 264 participants who failed to quit or relapsed were offered the opportunity to reengage in cessation. Of these, 134 were randomized into three reengagement conditions: (1) repeat initial intervention ("recycle"), (2) Smoking reduction with eventual cessation goal ("rate reduction"), or (3) Choose #1 or #2 ("choice"). Prolonged abstinence and 7-day point prevalence abstinence were measured at 12 months. RESULTS: Despite being in a clinical trial advertised as having the opportunity for reengagement, only 51% (134 of the 264) of participants who still smoked at 3-month follow-up were willing to reengage. Overall, participants randomized to recycle had higher prolonged cessation rates at 12 months than rate reduction conditions (OR = 16.43, 95% CI: 2.52 to 107.09, Bonferroni adjusted p = .011). When participants who randomly received recycle or rate reduction were pooled, respectively, with participants who chose recycle or rate reduction in the Choice group, recycle had higher prolonged cessation rates at 12 months than rate reduction (OR = 6.50, 95% CI: 1.49 to 28.42, p = .013). CONCLUSIONS: Our findings suggest service members and their family members who fail to quit smoking but are willing to reengage in a cessation program are more likely to benefit from repeating the same treatment. IMPLICATIONS: Finding methods that are both successful and acceptable to reengage people who smoke who want to quit can have a significant impact on improving the health of the public by reducing the portion of the population who smoke. This study suggests that repeating established cessation programs will result in more people ready to quit successfully achieving their goal.

Decreasing alcohol use among young adults presenting for service in the U.S. Air Force: An epidemiological surveillance study.

U.S. surveys demonstrate recent decreases in the prevalence of alcohol use and binge drinking among young adults. The current study aims to determine whether similar trends are evident in a similarly aged cohort of service members in the US Air Force to inform ongoing prevention efforts. Participants were 103,240 Air Force personnel in entry-level training between 2016 and 2019. Participants anonymously completed the AUDIT (Alcohol Use Disorder Identification Test) regarding their pre-service drinking. Logistic regression analyses and the Cochran-Armitage test were conducted to measure population trends over the study duration with stratification by age (<21 vs. ≥21) and evaluation of specific alcohol behaviors. Between 2016 and 2019, the proportion of young service members endorsing any alcohol use significantly decreased for both the <21 group (i.e. from 38.9% to 32.6%) and the ≥21 group (i.e. from 80.6% to 77.5%). Among those who endorsed drinking, a decrease over time in binge use was also observed from 46.6% to 37.8% for the <21 group and from 34.2% to 27.5% for the ≥21 group. Responses to other specific alcohol risk items and total AUDIT scores also demonstrated decreases. Binge use and risky drinking remained disproportionately common among those under the legal drinking age. It is encouraging to observe a shift toward abstinence and decreased binge use among this population of young military recruits. However, given the risk for many adverse health and legal consequences in this population, more work is needed to prevent problematic drinking, especially among those under the legal drinking age.

Cancer health self-efficacy improvement in a randomized controlled trial.

BACKGROUND: eHealth interventions can help cancer survivors self-manage their health outside the clinic. Little is known about how best to engage and assist survivors across the age and cancer treatment spectra. METHODS: The American Cancer Society conducted a randomized controlled trial that assessed efficacy of, and engagement with, Springboard Beyond Cancer, an eHealth self-management program for cancer survivors. Intent-to treat analyses assessed effects of intervention engagement for treatment (on-treatment vs completed) overall (n = 176; 88 control, 88 intervention arm) and separately by age (<60 years vs older). Multiple imputation was used to account for participants who were lost to follow-up (n = 41) or missing self-efficacy data (n = 1) at 3 months follow-up. RESULTS: Self-efficacy for managing cancer, the primary outcome of this trial, increased significantly within the intervention arm and for those who had completed treatment (Cohen's d = 0.26, 0.31, respectively). Additionally, participants with moderate-to-high engagement in the text and/or web intervention (n = 30) had a significantly greater self-efficacy for managing cancer-related issues compared to the control group (n = 68), with a medium effect size (Cohen's d = 0.44). Self-efficacy did not differ between the intervention and control arm at 3 months post-baseline. CONCLUSIONS: Study results suggest that cancer survivors benefit variably from eHealth tools. To maximize effects of such tools, it is imperative to tailor information to a priori identified survivor subgroups and increase engagement efforts.

Evaluating the feasibility of pharmacist-facilitated tobacco cessation interventions in independent community pharmacies in rural Appalachia.

BACKGROUND: Smoking rates in the United States are the highest in underserved rural regions. Thus, more points of contact are needed to link smokers to evidence-based cessation programs. OBJECTIVES: The purpose of this study was to conduct an evaluation to determine the feasibility, acceptability, and interest among rural pharmacists in implementing a pharmacist-facilitated smoking cessation program in independent community pharmacies in rural Appalachian communities in Virginia, North Carolina, Tennessee, and West Virginia. METHODS: This study utilized a complementary sequential mixed-methods approach to explore independent community pharmacists and technicians' experiences and beliefs about implementing a tobacco cessation program in their pharmacy. RESULTS: There were 49 pharmacists or technicians who completed the survey and 7 pharmacists who participated in the interviews. Four main findings emerged from the data: 1) pharmacies can help fill the gap in tobacco cessation services in rural communities, 2) under current practice, tobacco cessation resources when offered by independent community pharmacies are not always formalized, 3) there are known barriers, such as reimbursing for services, that need to be addressed to provide tobacco cessation in an independent pharmacy setting, and 4) the Ask-Advise-Connect model is a feasible tobacco cessation approach in a pharmacy. CONCLUSION: Although pharmacists may be ideally situated to build capacity for smoking cessation in rural areas, smoking cessation interventions need to use existing approaches that compensate pharmacists for their time spent counseling patients. Furthermore, simple documentation and billing systems are needed to maximize utilization of tobacco cessation products and services provided in the pharmacy.

Psychological, Normative, and Environmental Barriers to Tobacco Cessation that Disproportionally Affect Sexual Minority Tobacco Users.

INTRODUCTION: Sexual minority populations-particularly gay/lesbian and bisexual women-use tobacco at higher rates than their heterosexual peers. Evidence-based biopsychosocial interventions for tobacco cessation are available; however, research is lacking on the specific barriers to tobacco cessation in these populations. The purpose of this study is to describe the psychological, normative, and environmental barriers to cessation that disproportionally affect sexual minority tobacco users. METHODS: Data from wave 1 of the Population Assessment of Tobacco and Health were used to explore differences by sexual identity across psychosocial barriers and facilitators of tobacco cessation. The analytic sample consisted of current tobacco users (including cigarettes, e-cigarettes, cigars, cigarillos, pipes, hookah, dissolvable snus, and smokeless products). Psychosocial barriers/facilitators were modeled using logistic regression analyses, controlling for age, race/ethnicity, poverty, education, census region, and urbanicity and were stratified by sex. Models accounted for the complex study design and nonresponse. RESULTS: Substance use and internalizing/externalizing behavioral problems were more common among gay/bisexual men. Bisexual, but not gay/lesbian, women also had higher odds of these behavioral problems. Bisexual men and women reported less normative pressure to quit than their heterosexual peers (no differences in gay/lesbian tobacco users). Gay men had more environmental barriers to quit, being more likely to receive tobacco promotion materials, and live with another tobacco user. CONCLUSIONS: Several barriers to tobacco cessation were identified as disproportionally affecting sexual minority groups in this study; however, there were considerable differences between sexual minority men and women, as well as between gay and bisexual participants. IMPLICATIONS: Several important psychological, normative, and environmental barriers to tobacco cessation were identified that disproportionally affect sexual minorities. There was considerable heterogeneity in the prevalence and relative difference of these barriers across sexual minority subgroups, suggesting that community-based tobacco cessation programs should be responsive to differences in gay and bisexual men and women.

Engagement and Short-term Abstinence Outcomes Among Blacks and Whites in the National Cancer Institute's SmokefreeTXT Program.

INTRODUCTION: Text-messaging programs for smoking cessation improve abstinence outcomes in the general population. However, little is known about engagement and abstinence outcomes among African Americans in text-messaging smoking cessation programs. The current study compares engagement and abstinence between Blacks and Whites in the National Cancer Institute's SmokefreeTXT program. METHOD: Data were from Blacks (n = 1333) and Whites (n = 7154) who enrolled in the 6-week SmokefreeTXT program between August 2017 and June 2018. We assessed the association between race and program initiation and completion; responses to weekly smoking cessation, mood, and craving assessments; and self-reported abstinence using multivariable logistic regression. RESULTS: Blacks and Whites initiated the program at a similar frequency, yet Blacks were more likely to complete the program (adjusted odds ratio [AOR] = 1.71, 95% confidence interval [CI] = 1.43 to 2.06). Blacks were less likely to respond to all seven abstinence, mood, and craving assessments (eg, AOR of quit day responses = 0.63, 95% CI = 0.51 to 0.77; 6-week AOR = 0.50, 95% CI = 0.34 to 0.72). Self-reported abstinence was lower among Blacks for all seven smoking assessments (eg, quit day abstinence AOR = 0.52, 95% CI = 0.41 to 0.68; 6-week abstinence AOR = 0.58, 95% CI = 0.38 to 0.89). CONCLUSION: Although Blacks were more likely than Whites to complete the SmokefreeTXT program, they were less likely to engage with the program by responding to assessment questions and had lower abstinence rates. Qualitative research may reveal unique barriers to engagement among Blacks. IMPLICATIONS: Black smokers enrolled in a nationwide mobile smoking cessation program at a rate comparable to White smokers. However, they were significantly less likely to engage with the program or quit smoking. This study highlights the need to examine barriers to cessation for Black smokers.

The association between perceived e-cigarette and nicotine addictiveness, information-seeking, and e-cigarette trial among U.S. adults.

Perceptions of harm and addictiveness are associated with smoking combusted cigarettes, but these factors have not been fully explored for e-cigarettes. Specifically, little is known about the perceived harm and addictiveness of e-cigarettes, or whether information-seeking about e-cigarettes is related to trying e-cigarettes. We aimed to determine the relationship between (1) perceived e-cigarette harm and addictiveness and trying e-cigarettes; (2) nicotine perceptions and trying e-cigarettes; and (3) e-cigarette information-seeking, Internet use, and trying e-cigarettes. We used data from the nationally representative 2015 Health Information National Trends Survey-FDA (HINTS-FDA 2015). Weighted multivariable logistic regression models assessed independent associations between perceived e-cigarette harm, perceived e-cigarette addictiveness, nicotine perceptions, e-cigarette information-seeking, personal Internet use, and trying e-cigarettes, among 3195 adults. Compared to people who believed e-cigarettes were equally or more addictive than combusted cigarettes, those who believed e-cigarettes were less addictive had 2.49 times the odds of trying e-cigarettes (95% confidence interval [CI]: 1.30, 4.74). Perceived e-cigarette harm and nicotine perceptions were not associated with trying e-cigarettes. The positive association between e-cigarette addictiveness and trying e-cigarettes coupled with the lack of an association between nicotine perceptions and trying e-cigarettes suggests people do not fully understand that e-cigarettes contain nicotine and therefore could be addictive. People most frequently reported searching for information about potential health effects of e-cigarettes (37.9%), indicating that people are interested in learning about the potential impact of e-cigarette use on their health. People who searched for information about e-cigarettes had 10.23 higher odds of trying e-cigarettes (CI: 5.41, 19.33).

Associations Between Engagement and Outcomes in the SmokefreeTXT Program: A Growth Mixture Modeling Analysis.

INTRODUCTION: Smoking continues to be a leading cause of preventable death. Mobile health (mHealth) can extend the reach of smoking cessation programs; however, user dropout, especially in real-world implementations of these programs, limit their potential effectiveness. Research is needed to understand patterns of engagement in mHealth cessation programs. METHODS: SmokefreeTXT (SFTXT) is the National Cancer Institute's 6-8 week smoking cessation text-messaging intervention. Latent growth mixture modeling was used to identify unique classes of engagement among SFTXT users using real-world program data from 7090 SFTXT users. Survival analysis was conducted to model program dropout over time by class, and multilevel modeling was used to explore differences in abstinence over time. RESULTS: We identified four unique patterns of engagement groups. The largest percentage of users (61.6%) were in the low-engagers declining group; these users started off with low level of engagement and their engagement decreased over time. Users in this group were more likely to drop out from the program and less likely to be abstinent than users in the other groups. Users in the high engagers-maintaining group (ie, the smallest but most engaged group) were less likely to be daily smokers at baseline and were slightly older than those in the other groups. They were most likely to complete the program and report being abstinent. CONCLUSIONS: Our findings show the importance of maintaining active engagement in text-based cessation programs. Future research is needed to elucidate predictors of the various levels of engagement, and to assess whether strategies aimed at increasing engagement result in higher abstinence rates. IMPLICATIONS: The current study enabled us to investigate differing engagement patterns in non-incentivized program participants, which can help inform program modifications in real-world settings. Lack of engagement and dropout continue to impede the potential effectiveness of mHealth interventions, and understanding patterns and predictors of engagement can enhance the impact of these programs.

Association between high school students' cigarette smoking, asthma and related beliefs: a population-based study.

BACKGROUND: Smoking has a detrimental effect on the symptoms and severity of asthma, a common chronic disease among adolescents. The purpose of this study was to examine the association between asthma and smoking among high school students and assess provider-patient communication with asthmatic adolescents regarding smoking and adolescents' beliefs about the harms of smoking. METHODS: In fall 2014, data from high school students, ages 14-18 years, completing the 2009-2010 Virginia Youth Tobacco Survey (N = 1796) were used in descriptive analyses and multivariable logistic regression models adjusting for model-specific confounders as appropriate. RESULTS: Overall, an estimated 19 % of high school students in Virginia smoked and 16 % had asthma. Odds of smoking did not differ by asthma status; however, asthmatics had 1.5 times higher odds of being asked if they smoke (95 % CI 1.06-2.13) and being advised not to smoke by a health professional (95 % CI 1.10-2.14) compared to non-asthmatics. Asthmatics who believed second-hand smoke or smoking 1-5 cigarettes/day was not harmful had respectively 4.2 and 2.8 times higher odds of smoking than those who thought each was harmful. Further, asthmatics who thought smoking 1-2 years is safe had 3.4 times higher odds of smoking than those who did not (95 % CI 1.57-10.1). CONCLUSIONS: While asthmatic adolescents are just as likely to smoke as non-asthmatics, less healthy beliefs about the risks of smoking increase the odds of smoking among asthmatics. Thus, targeted asthma-specific smoking prevention and education to change attitudes and beliefs could be an effective tool for adolescents.

Survivorship care plans and time since diagnosis: factors that contribute to who breast cancer survivors see for the majority of their care.

PURPOSE: The study purpose is to describe who breast cancer survivors see for their care by years since diagnosis and determine the association between time since diagnosis and the type of clinician providing the majority of care, taking into account receipt of follow-up care instructions. METHODS: The 2010 Behavioral Risk Factor Surveillance System was used to identify a sample (N = 1024) of women with a self-reported history of breast cancer. Descriptive characteristics were calculated and stratified by years since diagnosis. Multivariate logistic regression adjusting for age, income, and receiving follow-up care instructions was performed to evaluate the association between years since diagnosis and clinician providing the majority of care. RESULTS: The type of clinician reported most frequently was a primary care provider. Women 0-1 year since diagnosis had the highest proportion reporting a cancer-related clinician for their care. After adjustment, women 0-1, 2-3, and 4-5 years since diagnosis were respectively 2.1, 2.6, and 1.7 times more likely to have a cancer-related clinician providing the majority of care compared to women 6+ years since diagnosis (respective 95 % confidence intervals (CIs) 1.0-4.3; 1.4-4.6; 0.9-3.1). CONCLUSIONS: Breast cancer survivors receive the majority of their care from primary care providers, and years since diagnosis has a significant impact on who survivors see for their care. Breast cancer survivors have nationally recommended follow-up guidelines; however, the type of clinician that should provide care is not specified. Information regarding who provides the majority of care can be used for future planning and policy development.

Identifying Patterns of Smoking Cessation App Feature Use That Predict Successful Quitting: Secondary Analysis of Experimental Data Leveraging Machine Learning.

BACKGROUND: Leveraging free smartphone apps can help expand the availability and use of evidence-based smoking cessation interventions. However, there is a need for additional research investigating how the use of different features within such apps impacts their effectiveness. OBJECTIVE: We used observational data collected from an experiment of a publicly available smoking cessation app to develop supervised machine learning (SML) algorithms intended to distinguish the app features that promote successful smoking cessation. We then assessed the extent to which patterns of app feature use accounted for variance in cessation that could not be explained by other known predictors of cessation (eg, tobacco use behaviors). METHODS: Data came from an experiment (ClinicalTrials.gov NCT04623736) testing the impacts of incentivizing ecological momentary assessments within the National Cancer Institute's quitSTART app. Participants' (N=133) app activity, including every action they took within the app and its corresponding time stamp, was recorded. Demographic and baseline tobacco use characteristics were measured at the start of the experiment, and short-term smoking cessation (7-day point prevalence abstinence) was measured at 4 weeks after baseline. Logistic regression SML modeling was used to estimate participants' probability of cessation from 28 variables reflecting participants' use of different app features, assigned experimental conditions, and phone type (iPhone [Apple Inc] or Android [Google]). The SML model was first fit in a training set (n=100) and then its accuracy was assessed in a held-aside test set (n=33). Within the test set, a likelihood ratio test (n=30) assessed whether adding individuals' SML-predicted probabilities of cessation to a logistic regression model that included demographic and tobacco use (eg, polyuse) variables explained additional variance in 4-week cessation. RESULTS: The SML model's sensitivity (0.67) and specificity (0.67) in the held-aside test set indicated that individuals' patterns of using different app features predicted cessation with reasonable accuracy. The likelihood ratio test showed that the logistic regression, which included the SML model-predicted probabilities, was statistically equivalent to the model that only included the demographic and tobacco use variables (P=.16). CONCLUSIONS: Harnessing user data through SML could help determine the features of smoking cessation apps that are most useful. This methodological approach could be applied in future research focusing on smoking cessation app features to inform the development and improvement of smoking cessation apps. TRIAL REGISTRATION: ClinicalTrials.gov NCT04623736; https://clinicaltrials.gov/study/NCT04623736.

Characterizing perceived usability and its correlation with smoking cessation: An analysis of user assessments of the smoking cessation app quitSTART.

Background: As smartphone ownership has become common in all demographic groups in the United States, smartphone applications (apps) for smoking cessation have grown in popularity due to their potential for supporting the diverse populations in the United States who are attempting to quit smoking. Usability is commonly assessed in mobile health (mHealth) technology as an important aspect of the user experience that could influence users' adherence to a health app and health outcomes. However, the variation of perceived usability across demographic groups, and the implications of that variation for app success, have not been well studied. Objective: The aims of this study were to characterize variation in the perceived usability of the National Cancer Institute Smokefree.gov Initiative smoking cessation app quitSTART across demographic groups, and to assess the correlation between perceived usability and short-term smoking cessation. Methods: We conducted a secondary analysis of data from a randomized controlled trial conducted from 2020 to 2021, which used a 16-item modified version of the mHealth App Usability Questionnaire (MAUQ) to quantify perceived usability four weeks after app download among 131 smokers attempting to quit. Responses were coded on a 5-point Likert-type scale ranging from strongly disagree (1) to strongly agree (5) and total perceived usability was calculated as the sum of all 16 items (range: 16-80). Associations between participant demographic characteristics (gender, race, education level, age, etc.) and total usability were determined using an ANCOVA model. A multivariable logistic regression model was used to assess the association between usability and smoking cessation, also assessed 4 weeks after app download. Results: The ANCOVA model demonstrated that race was associated with perceived usability, with participants from a racial minority group reporting higher total usability than White participants (p < 0.001). White participants had an adjusted mean total usability of 55.8 (95 % CI: 52.8, 58.8) while racial minority group participants had an adjusted mean total usability of 66.5 (95 % CI: 61.2, 71.6). Other participant demographic characteristics, such as gender and sexual minority status, were not associated with mean total usability. Total usability was positively associated with smoking cessation (OR: 1.04, 95 % CI: 1.00, 1.08, p = 0.031). Conclusions: Total perceived usability of quitSTART was higher among adults from a racial minority group compared to White adults, and perceived usability was positively associated with cessation success. These findings emphasize the importance of ensuring high usability of mHealth smoking cessation apps for diverse populations.

Unveiling rural and Appalachian disparities in cigarette smoking through the social vulnerability index and other county-level characteristics.

PURPOSE: Mitigating tobacco-related disparities in the Appalachian region and rural areas is crucial. This study seeks to gauge cigarette smoking prevalence in Virginia counties, uncover rurality and Appalachian-linked disparities, and explore local drivers of these gaps. METHOD: A 2011-2019 Virginia BRFSS data were used to estimate county-level cigarette smoking rates in adults aged 18 or older. Counties were categorized as urban/rural and Appalachian/non-Appalachian, with a focus on rural-Appalachian. Disparities in cigarette smoking rates and associated factors were analyzed via the Blinder-Oaxaca decomposition method. The study assessed 4 dimensions of the Centers for Disease Control and Prevention's social vulnerability index (SVI): socioeconomic, minority status, household composition, and housing. Additionally, county-specific factors such as tobacco agriculture, physician availability, coal mining, and tobacco retailer density were examined. FINDINGS: Rural areas exhibited a 6.18% higher cigarette smoking prevalence compared to urban areas (P<.001). SVI dimensions accounted for 53.2% of the disparity, county features explained 16.4%, and 30.4% remained unexplained. Appalachian areas had a 6.79% higher cigarette smoking prevalence than non-Appalachian areas (P<.001). SVI dimensions explained 51.4% of the disparity, county features accounted for 21.8%, leaving 26.8% unexplained. Rural-Appalachian areas showed a 7.8% higher cigarette smoking prevalence (P<.001). SVI dimensions contributed to 51.7% of the disparity, county features explained 9.6%, and 38.7% remained unexplained. CONCLUSIONS: Substantial disparities in cigarette smoking prevalence exist in underserved areas of Virginia, including rural, Appalachian, and rural-Appalachian regions. While SVI dimensions, physician availability, tobacco agriculture, and coal mining contribute, yet notable gaps remain unexplained. Targeted interventions must tackle unique challenges in disadvantaged areas to reduce smoking and promote health equity.

Testing the feasibility of the QuitAid smoking cessation intervention in a randomized factorial design in an independent, rural community pharmacy.

BACKGROUND: Adult smoking rates in the USA are highest in economically depressed rural Appalachia. Pharmacist-delivered tobacco cessation support that incorporates medication therapy management (such as the QuitAid intervention) is a promising approach to address this need. METHODS: Twenty-four adult smokers recruited between September and November 2021 through an independent pharmacy in rural Appalachia were randomized in a non-blinded 2 × 2 × 2 factorial design to (1) pharmacist delivered QuitAid intervention (yes vs. no); (2) combination nicotine replacement therapy (NRT) gum + NRT patch (vs. NRT patch); and/or (3) 8 weeks of NRT (vs. standard 4 weeks). Participants received 4 weeks of NRT patch in addition to the components to which they were assigned. Participants completed baseline and 3-month follow-up assessments. Primary outcomes were feasibility of recruitment and randomization, retention, treatment adherence, and fidelity. RESULTS: Participants were recruited in 7 weeks primarily through a referral process, commonly referred to as ask-advise-connect (61%). Participants were on average 52.4 years old, 29.2% were male and the majority were white (91.6%) and Non-Hispanic (91.7%). There was a high level of adherence to the interventions, with 85% of QuitAid sessions completed, 83.3% of the patch used, and 54.5% of gum used. Participants reported a high level of satisfaction with the program, and there was a high level of retention (92%). CONCLUSIONS: This demonstration pilot randomized controlled study indicates that an ask-advise-connect model for connecting rural smokers to smoking cessation support and providing QuitAid for smoking cessation is feasible and acceptable among rural Appalachian smokers and independent pharmacists. Further investigation into the efficacy of a pharmacist-delivered approach for smoking cessation is needed. TRIAL REGISTRATION: The trial was retrospectively registered at ClinicalTrials.gov. Trial #: NCT05649241.

Do Beliefs about Alcohol and Cancer Risk Vary by Alcoholic Beverage Type and Heart Disease Risk Beliefs?

BACKGROUND: Alcohol is a leading risk factor for cancer, yet awareness of the alcohol-cancer link is low. Awareness may be influenced by perceptions of potential health benefits of alcohol consumption or certain alcoholic beverage types. The purpose of this study was to estimate awareness of the alcohol-cancer link by beverage type and to examine the relationship between this awareness and concomitant beliefs about alcohol and heart disease risk. METHODS: We analyzed data from the 2020 Health Information National Trends Survey 5 Cycle 4, a nationally representative survey of U.S. adults. RESULTS: Awareness of the alcohol-cancer link was highest for liquor (31.2%), followed by beer (24.9%) and wine (20.3%). More U.S. adults believed wine (10.3%) decreased cancer risk, compared with beer (2.2%) and liquor (1.7%). Most U.S. adults (>50%) reported not knowing how these beverages affected cancer risk. U.S. adults believing alcoholic beverages increased heart disease risk had higher adjusted predicted probabilities of being aware of the alcohol-cancer link (wine: 58.6%; beer: 52.4%; liquor: 59.4%) compared with those unsure (wine: 6.0%; beer: 8.6%; liquor: 13.2%), or believing alcoholic beverages reduced (wine: 16.2%; beer: 21.6%; liquor: 23.8%) or had no effect on heart disease risk (wine: 10.2%; beer: 12.0%; liquor: 16.9%). CONCLUSIONS: Awareness of the alcohol-cancer link was low, varied by beverage type, and was higher among those recognizing that alcohol use increased heart disease risk. IMPACT: These findings underscore the need to educate U.S. adults about the alcohol-cancer link, including raising awareness that drinking all alcoholic beverage types increases cancer risk. See related commentary by Hay et al., p. 9.

Predictors of Re-Engagement after Relapse in a Tobacco Quit Line Intervention: Secondary Analysis from a Randomized Clinical Trial.

People who smoke often make several quit attempts before successfully maintaining abstinence. Therefore, incorporating re-engagement for people who fail to initially quit could increase quit attempts and ultimately increase cessation rates. Within the context of quit line-based interventions, it remains unknown what characteristics are associated with re-engagement. The purpose of this study was to assess associations between demographic and motivational characteristics, tobacco use, and initial intervention engagement with re-engagement in a tobacco quit line intervention. Among 372 adults who reported smoking three months after initiating a quit line-facilitated quit attempt as part of a larger randomized clinical trial, associations between personal characteristics (e.g., age, gender, nicotine dependence, and confidence in their ability to quit smoking) and initial intervention engagement (number of completed counseling sessions and use of nicotine replacement therapy (NRT)) with re-engagement (accepting an offer to re-initiate the quit line intervention) were determined using multivariable logistic regression modeling. Compared to non-White participants, White participants had lower odds of re-engaging (OR: 0.42, 95% CI: 0.23, 0.75). Number of initial counseling sessions completed was associated with re-engaging. NRT use during the initial intervention was not associated with re-engaging. Initial intervention engagement is important in the process of re-engagement, specifically attending counseling sessions. Exploration of associations between initial intervention engagement and potentially modifiable motivational factors is needed to be potentially leveraged in future interventions to maintain continued engagement in cessation among adults who smoke.

Tobacco quitline performance: Comparing the impacts of early cessation and proactive re-engagement on callers' smoking status at follow-up at 12 months.

INTRODUCTION: While tobacco Quitlines are effective in the promotion of smoking cessation, the majority of callers who wish to quit still fail to do so. The aim of this study was to determine if 12-month tobacco Quitline smoking cessation rates could be improved with re-engagement of callers whose first Quitline treatment failed to establish abstinence. METHODS: In an adaptive trial, 614 adult smokers, who were active duty, retired, and family of military personnel with TRICARE insurance who called a tobacco Quitline, received a previously evaluated and efficacious four-session tobacco cessation intervention with nicotine replacement therapy (NRT). At the scheduled follow-up at 3 months, callers who had not yet achieved abstinence were offered the opportunity to re-engage. This resulted in three caller groups: 1) those who were abstinent, 2) those who were still smoking but willing to re-engage with an additional Quitline treatment; and 3) individuals who were still smoking but declined re-engagement. A propensity score-adjusted logistic regression model was generated to compare past-7-day point prevalence abstinence at 12 months post Quitline consultation. RESULTS: Using a propensity score adjusted logistic regression model, comparison of the three groups resulted in higher odds of past-7-day point prevalence abstinence at follow-up at 12 months for those who were abstinent at 3 months compared to those who re-engaged (OR=9.6; 95% CI: 5.2-17.8; Bonferroni adjusted p<0.0001), and relative to those who declined re-engagement (OR=13.4; 95% CI: 6.8-26.3; Bonferroni adjusted p<0.0001). There was no statistically significant difference in smoking abstinence between smokers at 3 months who re-engaged and those who declined re-engagement (OR=1.39; 95% CI: 0.68-2.85). CONCLUSIONS: Tobacco Quitlines seeking to select a single initiative by which to maximize abstinence at follow-up at 12 months may benefit from diverting additional resources from the re-engagement of callers whose initial quit attempt failed, toward changes which increase callers' probability of success within the first 3 months of treatment. TRIAL REGISTRATION: This study is registered at clinicaltrials.gov (NCT02201810).

Integrating Tobacco Treatment Into Oncology Care: Reach and Effectiveness of Evidence-Based Tobacco Treatment Across National Cancer Institute-Designated Cancer Centers.

PURPOSE: Quitting smoking improves patients' clinical outcomes, yet smoking is not commonly addressed as part of cancer care. The Cancer Center Cessation Initiative (C3I) supports National Cancer Institute-designated cancer centers to integrate tobacco treatment programs (TTPs) into routine cancer care. C3I centers vary in size, implementation strategies used, and treatment approaches. We examined associations of these contextual factors with treatment reach and smoking cessation effectiveness. METHODS: This cross-sectional study used survey data from 28 C3I centers that reported tobacco treatment data during the first 6 months of 2021. Primary outcomes of interest were treatment reach (reach)-the proportion of patients identified as currently smoking who received at least one evidence-based tobacco treatment component (eg, counseling and pharmacotherapy)-and smoking cessation effectiveness (effectiveness)-the proportion of patients reporting 7-day point prevalence abstinence at 6-month follow-up. Center-level differences in reach and effectiveness were examined by center characteristics, implementation strategies, and tobacco treatment components. RESULTS: Of the total 692,662 unique patients seen, 44,437 reported current smoking. Across centers, a median of 96% of patients were screened for tobacco use, median smoking prevalence was 7.4%, median reach was 15.4%, and median effectiveness was 18.4%. Center-level characteristics associated with higher reach included higher smoking prevalence, use of center-wide TTP, and lower patient-to-tobacco treatment specialist ratio. Higher effectiveness was observed at centers that served a larger overall population and population of patients who smoke, reported a higher smoking prevalence, and/or offered electronic health record referrals via a closed-loop system. CONCLUSION: Whole-center TTP implementation among inpatients and outpatients, and increasing staff-to-patient ratios may improve TTP reach. Designating personnel with tobacco treatment expertise and resources to increase tobacco treatment dose or intensity may improve smoking cessation effectiveness.

Data envelopment analysis to evaluate the efficiency of tobacco treatment programs in the NCI Moonshot Cancer Center Cessation Initiative.

BACKGROUND: The Cancer Center Cessation Initiative (C3I) is a National Cancer Institute (NCI) Cancer Moonshot Program that supports NCI-designated cancer centers developing tobacco treatment programs for oncology patients who smoke. C3I-funded centers implement evidence-based programs that offer various smoking cessation treatment components (e.g., counseling, Quitline referrals, access to medications). While evaluation of implementation outcomes in C3I is guided by evaluation of reach and effectiveness (via RE-AIM), little is known about technical efficiency-i.e., how inputs (e.g., program costs, staff time) influence implementation outcomes (e.g., reach, effectiveness). This study demonstrates the application of data envelopment analysis (DEA) as an implementation science tool to evaluate technical efficiency of C3I programs and advance prioritization of implementation resources. METHODS: DEA is a linear programming technique widely used in economics and engineering for assessing relative performance of production units. Using data from 16 C3I-funded centers reported in 2020, we applied input-oriented DEA to model technical efficiency (i.e., proportion of observed outcomes to benchmarked outcomes for given input levels). The primary models used the constant returns-to-scale specification and featured cost-per-participant, total full-time equivalent (FTE) effort, and tobacco treatment specialist effort as model inputs and reach and effectiveness (quit rates) as outcomes. RESULTS: In the DEA model featuring cost-per-participant (input) and reach/effectiveness (outcomes), average constant returns-to-scale technical efficiency was 25.66 (SD = 24.56). When stratified by program characteristics, technical efficiency was higher among programs in cohort 1 (M = 29.15, SD = 28.65, n = 11) vs. cohort 2 (M = 17.99, SD = 10.16, n = 5), with point-of-care (M = 33.90, SD = 28.63, n = 9) vs. no point-of-care services (M = 15.59, SD = 14.31, n = 7), larger (M = 33.63, SD = 30.38, n = 8) vs. smaller center size (M = 17.70, SD = 15.00, n = 8), and higher (M = 29.65, SD = 30.99, n = 8) vs. lower smoking prevalence (M = 21.67, SD = 17.21, n = 8). CONCLUSION: Most C3I programs assessed were technically inefficient relative to the most efficient center benchmark and may be improved by optimizing the use of inputs (e.g., cost-per-participant) relative to program outcomes (e.g., reach, effectiveness). This study demonstrates the appropriateness and feasibility of using DEA to evaluate the relative performance of evidence-based programs.