Predictors Of Smoking Cessation Research Articles

Comparing the Fagerström Test and Heaviness of Smoking Index in Predicting Smoking Abstinence in Cancer Patients.

People with cancer who smoke exhibit greater cigarette dependence than people without cancer who smoke, a crucial factor in smoking cessation. Research is limited on the predictive potential of the Fagerström Test for Cigarette Dependence (FTCD) and the Heaviness of Smoking Index (HSI) on smoking abstinence in cancer patients undergoing smoking cessation treatment. We analyzed data from 5,934 cancer patients seeking smoking cessation treatment at The University of Texas MD Anderson Cancer Center (female 52.08%; Mean age = 55.52, SD = 11.17). We evaluated the predictive accuracy of FTCD and HSI on abstinence at 3-, 6-, and 9-months from first consultation, and assessed the concordance between these tools in measuring cigarette dependence using Cohen's kappa test and different correlation and regression models. We also analyzed variations across sex at birth and race/ethnicity. Both the FTCD and the HSI demonstrated comparable predictive accuracy for smoking cessation at all follow-ups, with neither showing high accuracy (Areas Under the Curve scores around 0.6). Concordance analysis revealed substantial agreement between FTCD and HSI scores (Cohen's kappa ~ 0.7), particularly at lower levels of dependence. However, this agreement varied by race, with reduced concordance observed in Non-Hispanic Blacks. Our results indicate that both the FTCD and HSI are effective tools for predicting smoking cessation in cancer patients, with the HSI offering a less burdensome assessment option. Nevertheless, the findings suggest the need for tailored approaches in assessing cigarette dependence that could predict smoking cessation more accurately, considering racial differences. The burden of assessing cigarette dependence in cancer care settings can be reduced by using the HSI instead of the FTCD. In addition, both instruments could be substantially interchanged and used for meta-analytic studies examining dependence and abstinence, but race/ethnicity should be considered.

Predictor of smoking cessation among school-going adolescents in Indonesia: a secondary analysis based on the transtheoretical model of behavioral change.

This study elucidates the complex journey of adolescents toward smoking cessation, investigating the association of relevant demographic factors, advertising, promotion, anti-cigarette messages, and individual knowledge and attitudes with being in different smoking cessation stages. Utilizing data from the 2019 Indonesia Global Youth Tobacco Survey, this secondary analysis included adolescents who reported ever smoking. The Transtheoretical Model (TTM) guided the categorization of the outcome variable into three smoking cessation stages based on the responses to two questions related to the intention and timing of the smoking cessation. This included contemplation, action, and maintenance stages. Multinomial logistic regression analyzed the associations between each independent variable and being in each stage of smoking cessation. The study comprised 3596 Indonesian adolescents from grades 7-12, of which 2484 responded to two questions related to intention and timing of smoking cessation and were included in regression analysis. Findings indicate that males and those aged ≥16 were predominantly in contemplation phase. Early smoking initiation, usage of other tobacco products, and exposure to various forms of smoke increased the likelihood of being in contemplation and action phases. Parental smoking, school smoking exposure, and second-hand smoke were significant contemplation phase predictors. Exposure to tobacco advertising was linked to an increased likelihood of being in contemplation and action phases, whereas anti-cigarette messages showed no significant impact. Awareness of cigarette and second-hand smoke harms reduced the odds of being in the contemplation phase, while enjoying smoking and willingness to accept cigarettes from friends increased the odds of being in contemplation and action phases rather than in maintenance phase. Addressing age, gender, cultural influences, environmental factors, and attitudes towards smoking through tailored interventions is vital for aiding smoking cessation in Indonesian adolescents. Strengthened tobacco control in schools and public places is recommended to bolster these efforts. Longitudinal studies are required to explore the evolving patterns of smoking cessation behaviors over time, enhancing our understanding of the factors influencing sustained cessation.

Concurrent E-cigarette Use While Enrolled in a Smoking Cessation Program: Associations Between Frequency of Use, Motives for Use, and Smoking Cessation.

Trial evidence suggests that e-cigarettes may aid in quitting smoking, while observational studies have found conflicting results. However, many observational studies have not adjusted for important differences between e-cigarette users and non-users. We aimed to determine the association between e-cigarette use frequency and motivation to use e-cigarettes to quit smoking, and smoking cessation using data from Canada's largest smoking cessation program. Participants who completed a baseline assessment and 6-month follow-up questionnaire were divided post hoc into four groups based on their self-reported e-cigarette use during the 30 days before baseline: (1) non-users; (2) users of e-cigarettes not containing nicotine; (3) occasional users; and (4) frequent users. Occasional and frequent users were further divided into two groups based on whether they reported using e-cigarettes to quit smoking. Abstinence at 6-month follow-up (7-day point prevalence abstinence) was compared among groups. Adjusted quit probabilities were significantly higher (both p < .001) for frequent baseline e-cigarette users (31.6%; 95% CI = 29.3%, 33.8%) than for non-users (25.8%; 25.3% and 26.3%) or occasional users (24.2%; 22.5% and 26.0%). Unadjusted proportions favored non-users over occasional users (p < .001), but this was not significant after adjustment (p = .06). People using e-cigarettes to quit smoking were not likelier than other users to be successful, but were likelier to report frequent e-cigarette use during follow-up. Frequent baseline e-cigarette use predicted successful smoking cessation, compared to occasional and non-users. Use of e-cigarettes to quit did not predict smoking cessation but was associated with continued use during follow-up, perhaps due in part to planned transitions to e-cigarettes. Prior observational studies investigating e-cigarette use for smoking cessation have found that occasional users have poorer outcomes than either frequent or non-users. Consistent with these studies, occasional users in our data also had poorer outcomes. However, after adjustment for variables associated with cessation success, we found that cessation probabilities did not differ between occasional and non-users. These findings are consistent with trial data showing the benefit of e-cigarette use among people trying to quit smoking. Results of this study suggest that differences between trials and previous observational studies may be because of unaddressed confounding in the latter.

Predicting outcomes of smoking cessation interventions in novel scenarios using ontology-informed, interpretable machine learning

Background Systematic reviews of effectiveness estimate the relative average effects of interventions and comparators in a set of existing studies e.g., using rate ratios. However, policymakers, planners and practitioners require predictions about outcomes in novel scenarios where aspects of the interventions, populations or settings may differ. This study aimed to develop and evaluate an ontology-informed, interpretable machine learning algorithm to predict smoking cessation outcomes using detailed information about interventions, their contexts and evaluation study methods. This is the second of two linked papers on the use of machine learning in the Human Behaviour-Change Project. Methods The study used a corpus of 405 reports of randomised trials of smoking cessation interventions from the Cochrane Library database. These were annotated using the Behaviour Change Intervention Ontology to classify, for each of 971 study arms, 82 features representing details of intervention content and delivery, population, setting, outcome, and study methodology. The annotated data was used to train a novel machine learning algorithm based on a set of interpretable rules organised according to the ontology. The algorithm was evaluated for predictive accuracy by performance in five-fold 80:20 cross-validation, and compared with other approaches. Results The machine learning algorithm produced a mean absolute error in prediction percentage cessation rates of 9.15% in cross-validation, outperforming other approaches including an uninterpretable ‘black-box’ deep neural network (9.42%), a linear regression model (10.55%) and a decision tree-based approach (9.53%). The rules generated by the algorithm were synthesised into a consensus rule set to create a publicly available predictive tool to provide outcome predictions and explanations in the form of rules expressed in terms of predictive features and their combinations. Conclusions An ontologically-informed, interpretable machine learning algorithm, using information about intervention scenarios from reports of smoking cessation trials, can predict outcomes in new smoking cessation intervention scenarios with moderate accuracy.

Using machine learning to extract information and predict outcomes from reports of randomised trials of smoking cessation interventions in the Human Behaviour-Change Project.

Background Using reports of randomised trials of smoking cessation interventions as a test case, this study aimed to develop and evaluate machine learning (ML) algorithms for extracting information from study reports and predicting outcomes as part of the Human Behaviour-Change Project. It is the first of two linked papers, with the second paper reporting on further development of a prediction system. Methods Researchers manually annotated 70 items of information ('entities') in 512 reports of randomised trials of smoking cessation interventions covering intervention content and delivery, population, setting, outcome and study methodology using the Behaviour Change Intervention Ontology. These entities were used to train ML algorithms to extract the information automatically. The information extraction ML algorithm involved a named-entity recognition system using the 'FLAIR' framework. The manually annotated intervention, population, setting and study entities were used to develop a deep-learning algorithm using multiple layers of long-short-term-memory (LSTM) components to predict smoking cessation outcomes. Results The F1 evaluation score, derived from the false positive and false negative rates (range 0-1), for the information extraction algorithm averaged 0.42 across different types of entity (SD=0.22, range 0.05-0.88) compared with an average human annotator's score of 0.75 (SD=0.15, range 0.38-1.00). The algorithm for assigning entities to study arms ( e.g., intervention or control) was not successful. This initial ML outcome prediction algorithm did not outperform prediction based just on the mean outcome value or a linear regression model. Conclusions While some success was achieved in using ML to extract information from reports of randomised trials of smoking cessation interventions, we identified major challenges that could be addressed by greater standardisation in the way that studies are reported. Outcome prediction from smoking cessation studies may benefit from development of novel algorithms, e.g., using ontological information to inform ML (as reported in the linked paper 3).

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

IntroductionPrior 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. MethodsThis 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. ResultsDual 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. ConclusionsThe 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.

Machine learning application for predicting smoking cessation among US adults: An analysis of waves 1-3 of the PATH study.

Identifying determinants of smoking cessation is critical for developing optimal cessation treatments and interventions. Machine learning (ML) is becoming more prevalent for smoking cessation success prediction in treatment programs. However, only individuals with an intention to quit smoking cigarettes participate in such programs, which limits the generalizability of the results. This study applies data from the Population Assessment of Tobacco and Health (PATH), a United States longitudinal nationally representative survey, to select primary determinants of smoking cessation and to train ML classification models for predicting smoking cessation among the general population. An analytical sample of 9,281 adult current established smokers from the PATH survey wave 1 was used to develop classification models to predict smoking cessation by wave 2. Random forest and gradient boosting machines were applied for variable selection, and the SHapley Additive explanation method was used to show the effect direction of the top-ranked variables. The final model predicted wave 2 smoking cessation for current established smokers in wave 1 with an accuracy of 72% in the test dataset. The validation results showed that a similar model could predict wave 3 smoking cessation of wave 2 smokers with an accuracy of 70%. Our analysis indicated that more past 30 days e-cigarette use at the time of quitting, fewer past 30 days cigarette use before quitting, ages older than 18 at smoking initiation, fewer years of smoking, poly tobacco past 30-days use before quitting, and higher BMI resulted in higher chances of cigarette cessation for adult smokers in the US.

Predicting smoking cessation, reduction and relapse six months after using the Stop-Tabac app for smartphones: a machine learning analysis

BackgroundAn analysis of predictors of smoking behaviour among users of smoking cessation apps can provide useful information beyond what is already known about predictors in other contexts. Therefore, the aim of the present study was to identify the best predictors of smoking cessation, smoking reduction and relapse six months after starting to use the smartphone app Stop-Tabac.MethodSecondary analysis of 5293 daily smokers from Switzerland and France who participated in a randomised trial testing the effectiveness of this app in 2020, with follow-up at one and six months. Machine learning algorithms were used to analyse the data. The analyses for smoking cessation included only the 1407 participants who responded after six months; the analysis for smoking reduction included only the 673 smokers at 6-month follow-up; and the analysis for relapse at 6 months included only the 502 individuals who had quit smoking after one month.ResultsSmoking cessation after 6 months was predicted by the following factors (in this order): tobacco dependence, motivation to quit smoking, frequency of app use and its perceived usefulness, and nicotine medication use. Among those who were still smoking at follow-up, reduction in cigarettes/day was predicted by tobacco dependence, nicotine medication use, frequency of app use and its perceived usefulness, and e-cigarette use. Among those who had quit smoking after one month, relapse after six months was predicted by intention to quit, frequency of app use, perceived usefulness of the app, level of dependence and nicotine medication use.ConclusionUsing machine learning algorithms, we identified independent predictors of smoking cessation, smoking reduction and relapse. Studies on the predictors of smoking behavior among users of smoking cessation apps may provide useful insights for the future development of these apps and future experimental studies.Clinical trial registrationISRCTN Registry: ISRCTN11318024, 17 May 2018. http://www.isrctn.com/ISRCTN11318024.

Prevalence and predictors of smoking cessation among smokers receiving smoking cessation intervention in primary care in Qatar: a 6-month follow-up study.

To estimate the rate and predictors of smoking cessation in smokers attending smoking cessation clinics in primary care settings in Qatar. A cross-sectional study was conducted among 759 smokers who had attended any of the 10 smoking cessation clinics in primary health care centers from January 2019 to June 2020. The sociodemographic, clinical, and smoking-related variables were assessed. Tailored behavioral and pharmacotherapy were delivered, and patients were interviewed at 6 months to estimate the 30-day point prevalence abstinence. To identify independent factors associated with smoking cessation, a multivariable logistic regression analysis was performed. The mean age of participants was 40.6 (±11.3), majority being married, Arab and employed, and having a tertiary education. Almost half of the smokers (48.7%) received varenicline alone, 42.6% received NRT, and 31.8% received a combination of both. The selection of drug therapy was based on preferences, experiences, and history of previously encountered adverse effects. The overall 30-day quit rate at 6 months follow-up was 32.4%. About three-quarters (72.5%) of participants had at least one quit attempt and 12.5% had 3 or more attempts. Later age at smoking initiation, lower cigarette consumption at baseline, lower CO concentration at baseline, use of smoking cessation pharmacotherapy, having made fewer quit attempts and non-exposure to secondhand smoke among friends were identified as significant predictors of successful quitting at 6 months. The 30-day quit rate at 6 months follow-up (32.4%) is comparable to the worldwide figure. However, further efforts should be made to plan cost-effective tobacco dependence treatment taking into account predictors and at-risk groups.

Self-efficacy in predicting smoking cessation: A prospective study in Italy.

Predicting the success of smoking cessation might be crucial to guide towards the treatment of smoking dependence in a clinical setting. We analyzed the potential determinants of successful smoking cessation with a specific focus on self-efficacy in predicting quitting smoking. All consecutive smokers (n=478; 224 men and 254 women) attending the Careggi University Hospital Smoking Cessation Service in Florence (Italy) in 2018-2019 provided information on self-efficacy in predicting smoking cessation, using a 1-10 rating scale during their first visit. Patients were followed up for success in quitting smoking at 3, 6 and 12 months, validated through CO exhaled measurement. To evaluate the association between self-efficacy and the probability of success, we estimated multivariable relative risks (RRs) and corresponding 95% confidence intervals (CIs) through log-binomial models for longitudinal data. Overall, 47.9% of smokers succeeded in their attempt to quit at 3 months, 40.2% at 6 months, and 33.9% at 12 months. Compared to low self-efficacy (rating scale 1-5), the RR of success in quitting smoking was 1.40 (95% CI: 1.06-1.85) for intermediate self-efficacy (scale 6-7) and 1.64 (95% CI: 1.28-2.12) for high self-efficacy (scale 8-10). Self-efficacy is an independent determinant of smoking cessation. We recommend to systematically collect self-efficacy, together with other relevant variables, to predict successful smoking cessation. Moreover, strategies to develop and maintain high levels of self-efficacy are essential to increase quit success and improve treatment.

Addiction or social need: towards a model to predict smoking cessation intentions

PurposeThe purpose of this study is to identify which social and psychological factors better determine intentions to quit smoking to inform public health policy.Design/methodology/approachData for this cross-sectional study were collected via face-to-face interaction following the pen-and-paper method. A total of 371 usable responses were received from randomly selected respondents of eight public sector universities located in the South Punjab province of Pakistan. Partial least squares structural equation modeling analysis was performed using SmartPLS program. A supplementary qualitative study, based on 21 in-depth interviews with the smokers, was conducted to augment findings of the quantitative study.FindingsResults showed that protection motivation theory and theory of planned behaviour are supported to predict intentions to quit smoking. Subjective norms have a greater influence on intentions to quit smoking than attitudes towards smoking cessation, while perceived behavioural control fails to predict intentions to quit smoking. Perceived rewards and perceived cost are significant in predicting attitudes towards smoking cessation, while extrinsic rewards predict intentions to quit smoking.Originality/valueThe existing models reported in the literature have sparsely investigated the cognitive (such as motivation and emotions) and social factors (such as social influence and behavioural controls) together as determinants of intentions to quit smoking, leaving room for more studies on an integrated model of these factors. This study takes the opportunity and proposes an integrated model encompassing psycho-social factors to predict tobacco consumption quitting behaviour in an emerging economy context.

Cumulative vulnerabilities and smoking abstinence: A test from a randomized clinical trial

Smoking cessation is often associated with socioeconomic and intrapersonal vulnerabilities such as psychopathology. Yet, most research that focuses on predicting smoking cessation outcomes tends focus on a small number of possible vulnerabilities. In a secondary data analysis, we developed and empirically evaluated a comprehensive, cumulative vulnerability risk composite reflecting psychologically based transdiagnostic processes, social determinants of health, and psychopathology. Participants were adult smokers who responded to study advertisements (e.g., flyers, newspaper ads, radio announcements) for an in-person delivered 4-session smoking cessation trial (N = 267; 47% female; Mage = 39.4, SD = 13.8). Results indicated that the decline in point prevalence abstinence (PPA) from quit week to 6-month post-quit was statistically significant (p < .001). There were statistically significant effects of cumulative risk score on the intercept (p < .001) and slope (p = .01). These findings were evident in unadjusted and adjusted (controlling for sex, treatment condition, and nicotine dependence) models. The present results indicate smokers with greater cumulative vulnerability demonstrated poorer smoking cessation outcomes. There may be clinical advantages to better understanding cumulative vulnerability among treatment-seeking smokers and other smoking populations to enhance the impact of public health efforts to reduce smoking.

Different clinical characteristics of current smokers and former smokers with asthma: a cross-sectional study of adult asthma patients in China

Smoking is a trigger for asthma, which has led to an increase in asthma incidence in China. In smokers, asthma management starts with smoking cessation. Data on predictors of smoking cessation in Chinese patients with asthma are scarce. The objective of this study was to find the differences in clinical characteristics between current smokers and former smokers with asthma in order to identify factors associated with smoking cessation. Eligible adults with diagnosed asthma and smoking from the hospital outpatient clinics (n = 2312) were enrolled and underwent a clinical evaluation, asthma control test (ACT), and pulmonary function test. Information on demographic and sociological data, lung function, laboratory tests, ACT and asthma control questionnaire (ACQ) scores was recorded. Patients were divided into a current smokers group and a former smokers group based on whether they had quit smoking. Logistic regression analysis was used to analyze the factors associated with smoking cessation. Of all patients with asthma, 34.6% were smokers and 65.4% were former smokers, and the mean age was 54.5 ± 11.5 years. Compared with current smokers, the former smokers were older, had longer duration of asthma, had higher ICS dose, had more partially controlled and uncontrolled asthma, had more pack-years, had smoked for longer, and had worse asthma control. The logistic regression model showed that smoking cessation was positively correlated with age, female sex, pack-years, years of smoking, partially controlled asthma, uncontrolled asthma, and body mass index (BMI), but was negatively correlated with ACT, FEV1, FEV1%predicted, and widowed status. More than 30% of asthma patients in the study were still smoking. Among those who quit smoking, many quit late, often not realizing they need to quit until they have significant breathing difficulties. The related factors of smoking cessation identified in this study indicate that there are still differences between continuing smokers and former smokers, and these factors should be focused on in asthma smoking cessation interventions to improve the prognosis of patients with asthma.

The Epidemiology of Smoking in Older Adults: A National Cohort Study.

Older smokers account for the greatest tobacco-related morbidity and mortality in the USA, while quitting smoking remains the single most effective preventive health intervention for reducing the risk of smoking-related illness. Yet, knowledge about patterns of smoking and smoking cessation in older adults is lacking. Assess trends in prevalence of cigarette smoking between 1998 and 2018 and identify patterns and predictors of smoking cessation in US older adults. Retrospective cohort study PARTICIPANTS: Individuals aged 55+ enrolled in the nationally representative Health and Retirement Study, 1998-2018 MAIN MEASURES: Current smoking was assessed with the question: "Do you smoke cigarettes now?" Quitting smoking was defined as having at least two consecutive waves (between 2 and 4 years) in which participants who were current smokers in 1998 reported they were not currently smoking in subsequent waves. Age-adjusted smoking prevalence decreased from 15.9% in 1998 (95% confidence interval (CI) 15.2, 16.7) to 11.2% in 2018 (95% CI 10.4, 12.1). Among 2187 current smokers in 1998 (mean age 64, 56% female), 56% of those living to age 90 had a sustained period of smoking cessation. Smoking less than 10 cigarettes/day was strongly associated with an increased likelihood of quitting smoking (subdistribution hazard ratio 2.3; 95% CI 1.9, 2.8), compared to those who smoked more than 20 cigarettes/day. Smoking prevalence among older persons has declined and substantial numbers of older smokers succeed in quitting smoking for a sustained period. These findings highlight the need for continued aggressive efforts at tobacco cessation among older persons.

Socioeconomic status across the life course and smoking cessation among older adult smokers in the U.S

IntroductionSocioeconomic status (SES) at different stages of the life course impacts late-life health. However, whether SES across the life course impacts smoking cessation in late-life is not known. Purpose: Assess how life course SES impacts smoking cessation among older smokers. MethodsWe identified 5,124 smokers, 50 years and older, from the 1998 to 2018 waves of the Health and Retirement Study. The outcome was self-reported smoking cessation. The main exposure was life course SES, defined as: low child and low adult SES (persistent low); low child, high adult SES (upward mobility); high child, low adult SES (downward mobility); and high child, high adult SES (persistent high). A multilevel mixed-effect logistic model was used to examine how life course SES predicts smoking cessation at age 65 and over time, adjusted for covariates. ResultsCompared to those with persistent high SES, those with persistent low SES, upward and downward SES were more likely to be Hispanic or non-Hispanic Black. The adjusted results showed that at age 65, compared to those with persistent high SES, those with persistent low SES (OR= 0.69, 95 % CI = [0.51–0.92]), upward SES (OR= 0.49, [0.32–0.75]), and downward SES (OR= 0.55, [0.40–0.76]) were less likely to quit. However, as age increased, only those with downward or persistent low SES were significantly less likely to quit compared to those with persistent high SES. DiscussionSocial mobility of SES from childhood to adulthood significantly impacts smoking cessation. Both stages of the life course should be considered to understand smoking behaviors.

Racial centrality mediates the association between adolescent racial discrimination and adult cigarette smoking outcomes among Black Americans

ObjectiveThe objective of this study was to investigate racial centrality as a mediator of the association between Black adolescents’ racial discrimination experiences and their cigarette use in early adulthood. MethodsThe data were drawn from the Family and Community Health Study, which is a longitudinal study of Black American families that began in 1996. Families with a child in 5th grade who identified as Black or African American were recruited from Iowa and Georgia. At baseline, there were 838 Black American children. Hierarchical regressions and bootstrap tests of the indirect effects were used to investigate whether racial centrality at Wave 5 (mean age = 21.6 years) mediated the association between adolescent discrimination at Waves 1–4 (mean ages = 10.5–18.8 years) and adult cigarette use at wave 6 (mean age = 23.5 years). ResultsBivariate associations indicated racial discrimination was significantly associated positively with racial centrality and adult use of cigarettes. Racial centrality indirectly affected the association between racial discrimination and cigarette use such that greater racial centrality was associated with less cigarette use. Further, racial centrality predicted cessation among those who had smoked. Finally, racial centrality was higher among those who never smoked and those who had smoked and quit, relative to those who currently smoke. ConclusionsThese findings suggest that having strong Black racial centrality is a mediator that reduces the risk of cigarette use among young adults who experience racial discrimination in adolescence. In addition, racial centrality also predicts smoking cessation among young Black Americans who smoke. Translational implications of these findings are discussed.

Ayahuasca and tobacco smoking cessation: results from an online survey in Brazil.

Smoking-related disease is a major problem globally. Effective smoking cessation treatments are however limited. Increasing evidence suggests that psychedelics have potential as treatments for substance use disorders and may therefore prove an option in aiding smoking cessation. To establish which factors predict smoking cessation in people who reported quitting or reducing smoking following ayahuasca consumption. A retrospective cross-sectional mixed-method study (quantitative and qualitative design) was undertaken using data from an online survey evaluating peoples' experiences before and after drinking ayahuasca. Multivariate logistic regression was performed with smoking condition (cessation or reduction/relapse) as a dependent variable and demographics, smoking, ayahuasca-related variables and the mystical experience (MEQ30) as predicting factors. A total of 441 responses were grouped according to self-reported smoking status: cessation (n = 305) or reduction/relapse (n = 136) smoking. Logistic regression showed that mystical experience (OR: 1.03; 95% CI [1.00-1.05]) and frequency of ayahuasca intake (OR: 2.16[1.00-4.70]) were protective factors, while positive mood (measured by the MEQ30) during the ayahuasca experience was a risk factor (OR: 0.91[0.85-0.97]). Qualitative thematic analysis identified eight themes (e.g. acquired awareness, spiritual experience, increased motivation) related to the ayahuasca experience and the process of smoking cessation/reduction. Our results suggest that ayahuasca could be used as a potential tool for smoking cessation, and that effects may be mediated by mystical experience. Given the current burden of smoking-related disease and the limited treatment options, studies are needed to investigate the efficacy of psychedelics in smoking cessation.

The Promise of Polygenic Risk Prediction in Smoking Cessation: Evidence From Two Treatment Trials.

Tobacco use disorder is a complex behavior with a strong genetic component. Genome-wide association studies (GWAS) on smoking behaviors allow for the creation of polygenic risk scores (PRSs) to approximate genetic vulnerability. However, the utility of smoking-related PRSs in predicting smoking cessation in clinical trials remains unknown. We evaluated the association between polygenic risk scores and bioverified smoking abstinence in a meta-analysis of two randomized, placebo-controlled smoking cessation trials. PRSs of smoking behaviors were created using the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN) consortium summary statistics. We evaluated the utility of using individual PRS of specific smoking behavior versus a combined genetic risk that combines PRS of all four smoking behaviors. Study participants came from the Transdisciplinary Tobacco Use Research Centers (TTURCs) Study (1091 smokers of European descent), and the Genetically Informed Smoking Cessation Trial (GISC) Study (501 smokers of European descent). PRS of later age of smoking initiation (OR [95% CI]: 1.20, [1.04-1.37], p = .0097) was significantly associated with bioverified smoking abstinence at end of treatment. In addition, the combined PRS of smoking behaviors also significantly predicted bioverified smoking abstinence (OR [95% CI] 0.71 [0.51-0.99], p = .045). PRS of later age at smoking initiation may be useful in predicting smoking cessation at the end of treatment. A combined PRS may be a useful predictor for smoking abstinence by capturing the genetic propensity for multiple smoking behaviors. There is a potential for polygenic risk scores to inform future clinical medicine, and a great need for evidence on whether these scores predict clinically meaningful outcomes. Our meta-analysis provides early evidence for potential utility of using polygenic risk scores to predict smoking cessation amongst smokers undergoing quit attempts, informing further work to optimize the use of polygenic risk scores in clinical care.

Improving smoking cessation after myocardial infarction by systematically implementing evidence-based treatment methods

We compared the odds of smoking cessation at 2-months post-myocardial infarction (MI), before and after implementing routines optimizing use of evidence-based smoking cessation methods, with start during admission. The following routines were implemented at six Swedish hospitals: cardiac rehabilitation nurses offering smokers consultation during admission, optimizing nicotine replacement therapy and varenicline prescription, and contacting patients by telephone during the 1st week post-discharge. Using logistic regression, odds for smoking cessation at 2-months before (n smokers/n admitted = 188/601) and after (n = 195/632) routine implementation were compared. Secondary outcomes included adherence to implemented routines and assessing the prognostic value of each routine on smoking cessation. After implementation, a larger proportion of smokers (65% vs. 54%) were abstinent at 2-months (OR 1.60 [1.04–2.48]). Including only those counselled during admission (n = 98), 74% were abstinent (2.50 [1.42–4.41]). After implementation, patients were more often counselled during admission (50% vs. 6%, p < 0.001), prescribed varenicline (23% vs. 7%, p < 0.001), and contacted by telephone post-discharge (18% vs. 2%, p < 0.001). Being contacted by telephone post-discharge (adjusted OR 2.74 [1.02–7.35]) and prescribed varenicline (adjusted OR 0.39 [0.19–0.83]) predicted smoking cessation at 2-months. In conclusion, readily available methods for aiding smoking cessation can be implemented effectively in routine practice, with beneficial effects for post-MI patients.

Precision Preventive Medicine of Relapse in Smoking Cessation: Can MRI Inform the Search of Intermediate Phenotypes?

Simple SummaryAddiction to tobacco is a serious health and economical problem because it is one of the most addictive and the most consumed substance in the world. Although well documented, and despite the desire of numerous smokers to quit, maintenance of abstinence is a daily challenge for most of them. The heterogeneity in achieving this maintenance raises the question of potential differences in brain reactivity. An emerging field of research has been interested in brain markers helping to identify individuals who are the most likely to relapse. Using brain imaging techniques such as Magnetic Resonance Imaging (MRI), one can hope it will be possible to offer tailored care for each patient.Chronic tobacco smoking remains a major health problem worldwide. Numerous smokers wish to quit but most fail, even if they are helped. The possibility of identifying neuro-biomarkers in smokers at high risk of relapse could be of incredible progress toward personalized prevention therapy. Our aim is to provide a scoping review of this research topic in the field of Magnetic Resonance Imaging (MRI) and to review the studies that investigated if MRI defined markers predicted smoking cessation treatment outcome (abstainers versus relapsers). Based on the available literature, a meta-analysis could not be conducted. We thus provide an overview of the results obtained and take stock of methodological issues that will need to be addressed to pave the way toward precision medicine. Based on the most consistent findings, we discuss the pivotal role of the insula in light of the most recent neurocognitive models of addiction.