Smoking Cessation and Incident Cardiovascular Disease.

Importance: The association between smoking cessation and cardiovascular disease (CVD) risk in relation to cumulative smoking exposure remains poorly understood. Objective: To evaluate the associations among smoking cessation, lifetime smoking burden, and CVD risk according to the number of years elapsed after smoking cessation. Design, Setting, and Participants: This retrospective cohort study of the Korean National Health Insurance Service database investigated smoking duration and intensity between January 2006 and December 2008. Participants were categorized by self-reported smoking habits as current, ex-, or never-smokers. Smoking records were updated every 2 years until December 2019, with participants whose smoking status changed or whose smoking status was unclear excluded. Data analysis was performed between June and December 2022. Exposures: Time-updated self-reported smoking status, years since quitting, and cumulative smoking amount (pack-years [PY]). Main Outcomes and Measures: The primary outcome was incidence and hazard ratio of CVD (composite of cardiovascular death, myocardial infarction, stroke, and heart failure). Results: Overall, 5 391 231 participants (39.9% male; mean [SD] age, 45.8 [14.7] years; 853 756 [15.8%] current smokers, 104 604 [1.9%] ex-smokers, and 4 432 871 [82.2%] never smokers) were followed up for a mean (SD) of 4.2 (4.4) years. The median (IQR) baseline cumulative smoking amounts were 14.0 (7.5-20.0) PY in current smokers and 10.5 (5.3-20.0) PY in ex-smokers. The median (IQR) duration of smoking cessation was 4 (2-8) years for ex-smokers. Regardless of continued smoking, a dose-dependent association was evident between smoking and incident CVD. Compared with current smokers, ex-smokers with a lifetime smoking burden of less than 8 PY (light ex-smokers) experienced a significant reduction in CVD risk within 10 years of cessation, with a CVD risk similar to that of never-smokers. Conversely, ex-smokers with at least 8 PY (heavy ex-smokers) exhibited a slower decline in CVD risk than light ex-smokers, requiring more than 25 years for the residual CVD risk to disappear. Conclusions and Relevance: In this cohort study, smoking and CVD risk exhibited a dose-dependent association, with light ex-smokers having a CVD risk similar to that of never-smokers relatively soon after smoking cessation. For heavy ex-smokers, greater than 25 years might be required for the residual CVD risk to align with that of never-smokers.

Prevalence and disparities in receiving medical advice to quit tobacco use in the US adult population.

Introduction: Evidence suggests that advice from health care professionals (HCP) increases the likelihood of quit attempts and successful quitting of tobacco use. However, previous studies primarily focussed on cigarette smoking and did not include all forms of tobacco products. This study aimed to investigate the prevalence and disparities in receiving HCP's advice to quit tobacco use (combustible or noncombustible) in the US adult population. Methods: Using the 2022 National Health Information Survey (NHIS) data, we examined 4,424 adults who reported (i) any tobacco product use within the past 12 months and (ii) having seen an HCP within the past 12 months. The outcome variable included the receipt of advice to quit tobacco use from an HCP, and predictors included sociodemographic variables. Weighted prevalence estimates were calculated, and multivariable regression analyses were conducted. Results: Over 38% of tobacco users who visited an HCP were advised to quit. The odds of receiving such advice were lower among Hispanics (AOR: 0.625; 95% confidence interval (CI) [0.464-0.843];p = 0.002), males (AOR: 0.767; 95% CI [0.659-0.893], p = 0.001), those above the poverty level (AOR: 0.795; 95% CI [0.641-0.987];p = 0.037), foreign-born (AOR: 0.664; 95% CI [0.496-0.888]; p = 0.006), those with a bachelor's degree or higher educational level (AOR: 0.477; 95% CI [0.349-0.653]; p < 0.001) and those aged less than 45 years (AOR: 0.404; 95% CI: [0.344-0.473]; p < 0.001). Conclusion: The prevalence of receiving HCP's advice to quit tobacco use remains suboptimal and disparate among sociodemographic groups. Our findings call for strategic implementation of the USPHS's recommendation on treating tobacco use and taking further actions to equip HCPs with the training and resources needed to provide appropriate advice to quit tobacco.

Feasibility and Engagement of a Mobile App Preparation Program (Kwit) for Smoking Cessation in an Ecological Context: Quantitative Study.

BACKGROUND: Mobile health apps can facilitate access to effective treatment and therapeutic information services. However, the real-world effectiveness of mobile apps for smoking cessation and their potential impact in everyday settings remain unclear. OBJECTIVE: In an ecological context, this study aimed to estimate the engagement rate of a mobile app-based smoking cessation preparation program and its potential impact on users' willingness, ability, and readiness to quit smoking. METHODS: A total of 2331 "organic users" (ie, users who discover and install a mobile app on their own, without any prompts) chose 1 of 2 program versions of the mobile app (Kwit): the basic version or the premium version. Both versions were identical in design, with 4 more evidence-based content items and strategies in the premium version. Outcomes were analyzed based on automated data registered in the app (engagement rate, motivation to quit, motivation type, motivation levels, and satisfaction level). Mann-Whitney and χ2 tests were used to compare the results of both groups. RESULTS: As expected, in the ecological context, a high dropout rate was observed at different moments. A significant difference was observed between the 2 versions (n=2331; χ21=5.4; P=.02), with a proportionally higher engagement rate in the premium version (premium=4.7% vs basic=2%). Likewise, differences were also observed between the 2 groups in terms of reasons to quit (n=2331; χ24=19; P≤.001; V=0.08), motivation type (n=2331; χ27=14.7; P=.04), and motivation level. Users of the app's premium version more frequently reported "well-being" (23.3% vs 17.9%) and "planning a pregnancy" (7.4% vs 4.4%) as their primary reasons for quitting smoking compared to those with the basic version. Moreover, they reported being more likely to be driven in the smoking cessation process by intrinsic motivation (premium=28% vs basic=20.4%), as well as feeling significantly more willing (z score=156,055; P≤.001; Cohen d=0.15), able (z score=172,905; P=.04; Cohen d=0.09), and ready (z score=166,390; P=.005; Cohen d=0.12) to stop smoking than users who had the basic version before completion of the preparation program. Among participants who finished each version of the program (premium: 9/189, 4.8%; basic: 47/2142, 2.19%), significant improvements in motivation levels were observed in both groups, although in different areas for each group (willingness levels for the premium group and ability for the basic group). CONCLUSIONS: These results suggest that even in ecological contexts where engagement rates are meager, the Kwit preparation program can address ambivalence by increasing willingness to change, self-confidence, and readiness to quit among its users, especially those who feel less able to do so. Further development and evaluations are needed to better understand determinants for regular mobile health apps.

Leveraging information from secondary endpoints to enhance dynamic borrowing across subpopulations.

Randomized trials seek efficient treatment effect estimation within target populations, yet scientific interest often also centers on subpopulations. Although there are typically too few subjects within each subpopulation to efficiently estimate these subpopulation treatment effects, one can gain precision by borrowing strength across subpopulations, as is the case in a basket trial. While dynamic borrowing has been proposed as an efficient approach to estimating subpopulation treatment effects on primary endpoints, additional efficiency could be gained by leveraging the information found in secondary endpoints. We propose a multisource exchangeability model (MEM) that incorporates secondary endpoints to more efficiently assess subpopulation exchangeability. Across simulation studies, our proposed model almost uniformly reduces the mean squared error when compared to the standard MEM that only considers data from the primary endpoint by gaining efficiency when subpopulations respond similarly to the treatment and reducing the magnitude of bias when the subpopulations are heterogeneous. We illustrate our model's feasibility using data from a recently completed trial of very low nicotine content cigarettes to estimate the effect on abstinence from smoking within three priority subpopulations. Our proposed model led to increases in the effective sample size two to four times greater than under the standard MEM.

Assessment of Smoking Behaviour of Adolescents in Two Districts of Nepal and Implications of the MPOWER Policy Measures: A Mixed Method Study.

INTRODUCTION: Tobacco use is the single most preventable cause of death and disease worldwide. This study aimed to assess the smoking behaviour of adolescents in two districts of Nepal and examine the implications of the MPOWER policy on their smoking behaviour. METHODS: An explanatory, mixed-method study was conducted in two districts of Nepal. Qualitative in-depth interviews were conducted among nine participants aged 35-50, representatives of the Ministry of Health, academic institutions, and managers of organisations working in tobacco control, using snowball sampling method. A total of 306 students of age 13-15 years, from six government schools were recruited through simple random sampling method using an adapted version of the Global Youth Tobacco Survey (GYTS). Findings of the qualitative and quantitative study were explained for concurrence and relevance to present overall study findings. Ethical approval was received from the Nepal Health Research Council and Mahidol University. RESULTS: Findings from the survey reported that a total of 25 (8.10%) of respondents were smokers, from which 13 (4.20%) were current smokers and 12 (3.90%) were ever smokers. Socio-economic status played a crucial role in the smoking behaviour. Although the survey among adolescents indicated an awareness of the policies, there was a lack of cessation services, which was concurrent with the qualitative findings. The interviews recommended improvements in the implementation of policy ban on public smoking, taxation, and availability of cessation services. CONCLUSIONS: The MPOWER policies are not regulated strictly, especially in areas of the ban on public smoking, regulating the selling of cigarettes to adolescents <18 years, and availability of cessation services.

Smoking cessation and mortality risk reduction in older adults with long-term smoking history.

BACKGROUND: The association between smoking cessation and decreased mortality existed among former smokers has been well documented. However, evidence is limited for smokers with long-term exposure. This study aims to quantify the association between smoking cessation and mortality by years since quitting in older adults with long-term smoking history. METHODS: Data from Beijing Healthy Aging Cohort Study (BHACS), conducted among communities aged over 55 years old at recruitment, were collected via questionnaire between July 2009 and September 2015 and followed up for all-cause and cancer mortality until March 2021. Self-reported smoking status and years since quitting were collected at baseline. Cox proportional hazards models were used to examine the association between smoking cessation and all-cause and cancer mortality. RESULTS: A total of 11 235 participants (43.9% male) were included, with a mean age of 70.35 (SD 7.71) years. Former smokers comprised 31.7% of the cohort, with a median smoking duration of 43 (IQR: 34-50) years. During 71 573 person-years of follow-up, there were 1 617 deaths (14.4% of the total cohort), of which 872 (17.7%) occurred among male participants. Compared with never smokers, HR (95%CI) for participants who current smoked was 2.898 (2.092-4.013); quit smoking less than 10 years (medians [quartiles] 4 [1, 7] years) before recruitment was 2.738(1.972-3.802); 10 to 20 years (16 [13, 20] years), 1.807(1.286-2.540); and 20 years or more (30 [25, 37] years), 1.293(0.981-1.705). The risk of all-cause and cancer mortality decreased gradually over years since quitting. Quitting less than 10 years, 10 to 20 years and 20 years or more, former smokers avoided an estimated 8.4%, 57.5% and 84.6% of excess all-cause mortality associated with current smoking, respectively. The association between smoking cessation and decreased mortality was observed among former smokers regardless of smoking history. CONCLUSIONS: In this study, current smoking was associated with nearly triple the mortality risk compared to never smoking. Smoking cessation, even after a long-term smoking history, was associated with significant decreases in the relative excess mortality linked to continuing smoking. The association were more pronounced in men.

Initial Evaluation of Acceptability, Engagement, and Effectiveness of the MO App to Provide Tailored and Comprehensive Support for Smoking Cessation: Development and Usability Study.

BACKGROUND: Despite the growing availability of smoking cessation apps, low engagement and cessation rates have remained a significant challenge. To address this issue, we used a user-centered design to iteratively develop a mobile app (MO) to provide comprehensive, tailored, and evidence-based content to support smokers in their quitting journey. OBJECTIVE: This study examined the acceptability, use, and preliminary efficacy of the MO app for smoking cessation. Specifically, we sought to understand smokers' preferred features, engagement, and satisfaction with MO; identify concerns in using the app and ways to improve the app; and evaluate its smoking cessation outcomes. METHODS: Through 3 cohorts, we recruited 10, 12, and 85 adult smokers who attempted to quit smoking to pilot-test the MO app between December 2019 and July 2022. Participants were instructed to complete a baseline survey, interact with the app for 6 weeks, and fill in a postsurvey at week 6. Participants in cohort 3 completed an additional postsurvey at week 12. Participants' app use was tracked and analyzed. The primary outcome measures were participants' 7-day point prevalence abstinence at 6 and 12 weeks. RESULTS: Participants reported high levels of satisfaction with the MO app across all 3 cohorts, rating it between 4.40 and 4.76 on a scale of 5 for acceptability. Users engaged with app activities for an average of 89 to 159 times over 35 days. The most liked features of the app included "quit plan," "tracking," "reminders and notifications," "MOtalks," and "motivational quotes." The 7-day point prevalence abstinence rate of the modified intention to treat population in cohort 3 was 58% at 6 weeks and 52% at 12 weeks. Those who interacted more frequently with app features and engaged with more diverse activities were more likely to maintain abstinence at weeks 6 and 12. For each additional time logged into the app, the odds of staying abstinent at week 12 increased by 5% (odds ratio [OR] 1.05, 95% CI 1.01-1.08). Participants who earned >5000 points during app use also had higher odds of quitting at both 6 weeks (OR 3.12, 95% CI 1.25-7.75) and 12 weeks (OR 4.65, 95% CI 1.83-11.76), compared with those who earned <5000 points. CONCLUSIONS: Our study demonstrated that MO is a feasible mobile phone app with high acceptability and usability and can effectively deliver smoking cessation support to individuals who want to quit. Implications for developing and evaluating mobile phone apps for smoking cessation are discussed.

Changes in the Success and Characteristics of Tobacco Dependence Treatment before and during the COVID-19 Pandemic: Clinical Sample Comparisons.

Background and Objectives: There is little information on changes in the process and outcomes of intensive tobacco dependence treatment during the COVID-19 pandemic. The following characteristics were evaluated: interest in treatment, the number of face-to-face or telephone follow-ups, the duration of pharmacotherapy use, and the success rate. The aim of our study was to compare the number of patients who entered tobacco dependence treatment programmes and evaluate the one-year success rate in patients three years before and three years after the COVID-19 pandemic. Materials and Methods: A single-site retrospective cohort study using data from patients treated at the Centre for Tobacco Dependence in Prague, Czech Republic, between 2017 and 2022 (n = 2039) was performed. The one-year abstinence rate was validated by measuring carbon monoxide in exhaled air (6 ppm cut-off). Patients were divided into two groups: the group for which treatment was initiated in 2017-2019 (i.e., before the COVID-19 pandemic, BC; n= 1221) and the group for which treatment was initiated in 2020-2022 (i.e., during the COVID-19 pandemic, DC; n = 818). Results: No significant differences in the success rate of tobacco dependence treatment were found between the two groups (BC group, 40.5% (494/1221) vs. DC group, 42.2% (345/818)) (χ2 (1, N = 2.039) = 0.6, p = 0.440). Furthermore, differences were not found in sex, education level, age at first cigarette, the duration of pharmacotherapy use, or the number of in-person visits. In contrast, there was an increase in the number of telephone contacts between the groups (18.7% (SD = 17.5%) vs. 32.9% (SD = 18.2%), p < 0.001). Conclusions: The number of patients who started treatment during the COVID-19 pandemic decreased by one-third compared to that during the 3-year period before the pandemic. The overall treatment success rate did not change significantly even with the increase in the number of telephone visits with the therapist.

Vaping: The Key to Quitting Cigarettes or a Gateway to Addiction?

Background and Objectives: In 2022, the Centers for Disease Control and Prevention (CDC) discovered that 2.55 million middle and high school students were using electronic cigarettes (e-cigarettes) in the US alone. E-cigarettes allow people to inhale a nicotine solution (e-liquid) into their bodies. While e-cigarettes are often advertised as a way to help people quit traditional tobacco products, the World Health Organization (WHO) has stated that there is no proof of e-cigarettes being effective at reducing an individual's tobacco use. The objective of this study is to determine why adolescents start smoking e-cigarettes. Materials and Methods: For the following study, the National Youth Tobacco Survey (NYTS) 2021 was used. This is a nationally representative cross-sectional survey that includes middle and high school students. The data were analyzed using SAS v.9.4. The odds ratio for smoking e-cigarettes was evaluated for both sex and race via a logistic regression. Results: It was discovered that the percentage of only e-cigarette smokers (12.10%) was 5.5 times the percentage of only traditional smokers (2.19%). Additionally, the median age at which adolescents started smoking e-cigarettes was fifteen years with an IQR (Inter Quartile Range) of two. We used a logistic regression to show that biological sex and race were significant predictors of e-cigarette usage. Conclusions: In general, we saw that adolescents were mostly starting to smoke e-cigarettes because their friends were doing it, out of curiosity, they were depressed or anxious, and/or to get a "high", implying that adolescents mostly started smoking in order to fit in and/or to numb themselves. Interestingly, reasons like cost, using them to quit smoking, seeing them in the media, and ease of attainment were ranked the lowest when it came to smoking e-cigarettes. However, their lower costs naturally lead to increased accessibility compared to traditional cigarettes, making them easier to reach the hands of teenagers. Overall, in this paper, we aim to identify if adolescents are choosing e-cigarettes as their first foray into tobacco products and why.

e-Cigarettes, Smoking Cessation, and Weight Change: Retrospective Secondary Analysis of the Evaluating the Efficacy of e-Cigarette Use for Smoking Cessation Trial.

BACKGROUND: While smoking cessation has been linked to substantial weight gain, the potential influence of e-cigarettes on weight changes among individuals who use these devices to quit smoking is not fully understood. OBJECTIVE: This study aims to reanalyze data from the Evaluating the Efficacy of e-Cigarette Use for Smoking Cessation (E3) trial to assess the causal effects of e-cigarette use on change in body weight. METHODS: This is a secondary analysis of the E3 trial in which participants were randomized into 3 groups: nicotine e-cigarettes plus counseling, nonnicotine e-cigarettes plus counseling, and counseling alone. With adjustment for baseline variables and the follow-up smoking abstinence status, weight changes were compared between the groups from baseline to 12 weeks' follow-up. Intention-to-treat and as-treated analyses were conducted using doubly robust estimation. Further causal analysis used 2 different propensity scoring methods to estimate causal regression curves for 4 smoking-related continuous variables. We evaluated 5 different subsets of data for each method. Selection bias was addressed, and missing data were imputed by the machine learning method extreme gradient boosting (XGBoost). RESULTS: A total of 257 individuals with measured weight at week 12 (mean age: 52, SD 12 y; women: n=122, 47.5%) were included. Across the 3 treatment groups, of the 257 participants, 204 (79.4%) who continued to smoke had, on average, largely unchanged weight at 12 weeks, with comparable mean weight gain ranging from -0.24 kg to 0.33 kg, while 53 (20.6%) smoking-abstinent participants gained weight, with a mean weight gain ranging from 2.05 kg to 2.70 kg. After adjustment, our analyses showed that the 2 e-cigarette arms exhibited a mean gain of 0.56 kg versus the counseling alone arm. The causal regression curves analysis also showed no strong evidence supporting a causal relationship between weight gain and the 3 e-cigarette-related variables. e-Cigarettes have small and variable causal effects on weight gain associated with smoking cessation. CONCLUSIONS: In the E3 trial, e-cigarettes seemed to have minimal effects on mitigating the weight gain observed in individuals who smoke and subsequently quit at 3 months. However, given the modest sample size and the potential underuse of e-cigarettes among those randomized to the e-cigarette treatment arms, these results need to be replicated in large, adequately powered trials. TRIAL REGISTRATION: ClinicalTrials.gov NCT02417467; https://www.clinicaltrials.gov/study/NCT02417467.

Stopping use of E-cigarettes and smoking combustible cigarettes: findings from a large longitudinal digital smoking cessation intervention study in the United States.

OBJECTIVE: Digital interventions have been widely implemented to promote tobacco cessation. However, implementations of these interventions have not yet considered how participants' e-cigarette use may influence their quitting outcomes. We explored the association of e-cigarette use and quitting smoking within the context of a study testing a digital tobacco cessation intervention among individuals in the United States who were 18 years and older, smoked combustible cigarettes, and enrolled in the intervention between August 2017 and March 2019. RESULTS: We identified four e-cigarette user groups (n = 990) based on the participants' baseline and six-month e-cigarette use (non-users, n = 621; recently started users, n = 60; sustained users, n = 187; recently stopped users, n = 122). A multiple logistic regression was used to estimate the adjusted odds ratios (AOR) of six-month quit outcome and the e-cigarette user groups. Compared to e-cigarette non-users, the odds of quitting smoking were significantly higher among recently stopped users (AOR = 1.68, 95% CI [1.06, 2.67], p = 0.03). Participants who were most successful at quitting combustible cigarettes also stopped using e-cigarettes at follow-up, although many sustained using both products. Findings suggest that digital tobacco cessation interventions may carefully consider how to promote e-cigarette use cessation among participants who successfully quit smoking. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT03224520 (July 21, 2017).

Beyond lung cancer screening, an opportunity for early detection of chronic obstructive pulmonary disease and cardiovascular diseases.

BACKGROUND: Lung cancer screening programs concern smokers at risk for cardiovascular diseases (CVDs) and chronic obstructive pulmonary disease (COPD). The LUMASCAN (LUng Cancer Screening, MArkers and low-dose computed tomography SCANner) study aimed to evaluate the acceptability and feasibility of screening for these 3 diseases in a community population with centralized organization and to determine low-dose computed tomography (CT) markers associated with each disease. METHODS: This cohort enrolled participants meeting National Comprehensive Cancer Network criteria (v1.2014) in an organized lung cancer-screening program including low-dose CT scans; spirometry; evaluations of coronary artery calcifications (CACs); and a smoking cessation plan at inclusion, 1, and 2 years; then telephone follow-up. Outcomes were the participation rate and the proportion of participants affected by lung cancer, obstructive lung disease, or CVD events. Logistic-regression models were used to identify radiological factors associated with each disease. RESULTS: Between 2016 and 2019, a total of 302 participants were enrolled: 61% men; median age 58.8 years; 77% active smoker; 11% diabetes; 38% hypertension; and 27% taking lipid-lowering agents. Inclusion, 1-year, and 2-year participation rates were 99%, 81%, 79%, respectively. After a median follow-up of 5.81 years, screenings detected 12 (4%) lung cancer, 9 of 12 via low-dose CT (78% localized) and 3 of 12 during follow-up (all stage IV), 83 (27%) unknown obstructive lung disease, and 131 (43.4%) moderate to severe CACs warranting a cardiology consultation. Preexisting COPD and moderate to severe CACs were associated with major CVD events with odds ratios of 1.98 (95% confident interval [CI] = 1.00 to 3.88) and 3.27 (95% CI = 1.72 to 6.43), respectively. CONCLUSION: The LUMASCAN study demonstrated the feasibility of combined screening for lung cancer, COPD, and CVD in a community population. Its centralized organization enabled high participation and coordination of healthcare practitioners.

Risk of smoking cessation treatment dropout: a cohort to help (re)think care.

OBJECTIVES: to evaluate the relative risk of smoking cessation treatment dropout during its intensive phase. METHODS: a retrospective and quantitative cohort study was developed from the electronic medical records of individuals who started smoking cessation treatment between 2015 and 2019 at a specialty clinic in a city in the interior of São Paulo, Brazil. The relative risk of dropping out of treatment was calculated using the Poisson regression model. RESULTS: it was observed that out of the 396 (100.0%) individuals who started the treatment, 109 (27.5%) abandoned it before the end of the intensive phase. For each one-year increase in age, the risk of dropping out of smoking cessation treatment decreased by an average of 2%. CONCLUSIONS: the risk of dropping out of smoking cessation treatment is higher among younger individuals. It is necessary to rethink the care offered to younger adults to promote the continuity of treatment.

Patterns and perceptions of vaping among adults living in social housing: a representative survey in Great Britain, 2023.

BACKGROUND: Vaping products are effective for helping people to stop smoking and may therefore offer a potential means to reduce high rates of smoking in socioeconomically disadvantaged groups. This study aimed to examine current patterns and perceptions of vaping among people living in social housing in Great Britain compared with those living in other housing types. METHODS: Data were from the Smoking Toolkit Study; a nationally-representative survey conducted in 2023 (n = 23,245). Logistic regression tested cross-sectional associations between living in social (vs. other) housing and current vaping among adults; vaping frequency, device type, nicotine concentration, and source of purchase among current vapers; use of vaping products as a smoking cessation aid among past-year smokers who tried to quit; and harm perceptions of vaping products relative to cigarettes among current smokers. RESULTS: Current vaping prevalence was twice as high among adults living in social housing (19.4%) compared with those in other housing types (10.4%; OR = 2.07, 95%CI = 1.84-2.33). This was partly explained by differences in sociodemographic characteristics and smoking status; after adjustment, the odds of being a current vaper were 33% higher (ORadj=1.33, 95%CI = 1.14-1.54). Among vapers, there were no notable differences by housing tenure in vaping frequency, main device type used, usual nicotine concentration, usual source of purchase, or use as a smoking cessation aid. However, current smokers living in social housing were more likely to think vaping is more harmful than cigarettes (31.6% vs. 21.8%; ORadj=1.61, 95%CI = 1.30-1.99). CONCLUSIONS: In Great Britain, adults who live in social housing are more likely to vape than those who live in other housing types, even after accounting for their younger age and higher smoking rates. However, misperceptions about the relative harms of vaping products and tobacco are common among smokers living in social housing. Interventions addressing these misperceptions could help encourage more people living in social housing to switch from smoking to vaping and reduce smoking-related health inequalities. PRE-REGISTRATION: The study protocol and analysis plan were pre-registered on Open Science Framework (https://osf.io/n3mvs/).

Smoking cessation and risk of metabolic syndrome: A meta-analysis.

BACKGROUND: Smoking is an important risk factor for various metabolic and cardiovascular disorders, and smoking cessation reduces the risk of these conditions. However, weight gain is commonly observed when individuals quit smoking, which often leads to hesitation in pursuing smoking cessation. Weight gain increases the risk of metabolic syndrome (MS). However, previous studies that investigated the relationship between smoking cessation and MS have yielded inconsistent results. Therefore, we conducted a meta-analysis to evaluate the association between smoking cessation and MS. METHODS: Medline, Embase, Cochrane Library and CINAHL databases, were comprehensively searched from inception to April 2023, to identify relevant studies examining the relationship between smoking cessation and MS, comparing such relationship to that with active smoking. The methodological quality of the selected studies was assessed using the Newcastle-Ottawa Quality Assessment Scale. A random-effects model was used for meta-analysis. RESULTS: Of 495 identified studies, 24 were reviewed. The risk of selection bias was identified in all the studies. The overall analysis of 14 studies, including data of combined results for both men and women, revealed an increased risk of MS among ex-smokers compared with that among active smokers (pooled relative risk [RR] 1.18, 95% confidence interval [CI]: 1.08-1.29). From the selected studies, 13 studies analyzing men were extracted for subgroup analysis. Among men, no significant difference in the risk of developing MS was observed between ex-smokers and smokers (pooled RR: 1.05, 95% CI: 0.95-1.17). In men, the risk of MS increased if the cessation period was ≤15 years in men (pooled RR 1.26, 95% CI: 1.01-1.56) and slightly decreased if the cessation period was > 15 years (RR 0.84, 95% CI: 0.70-1.00) in ex-smokers compared with that in current smokers. CONCLUSION: An increased risk of MS was observed in the early stages of smoking cessation compared with current smoking. As the longer duration of smoking cessation, the risk of MS becomes less significant.

Trends in smoking initiation and cessation over a century in two Australian cohorts.

BACKGROUND: Historical data on smoking can enhance our comprehension of the effectiveness of past tobacco control policies and play a key role in developing targeted public health interventions. This study was undertaken to assess trends in smoking initiation and cessation in Australia for the period 1910-2005. METHODS: Rates of smoking initiation and cessation were calculated for participants in two population-based cohorts, the Busselton Health Study and the Tasmanian Longitudinal Health Study. The effects of time trends, gender and age group were evaluated. RESULTS: Of the 29,971 participants, 56.8% ever smoked. In males, over the period 1910-1999, the rate of smoking initiation in young adolescents remained high with a peak in the 1970s; in older adolescents it peaked in the 1940s and then declined; in young adults it showed a steady decline. In females, the rate of smoking initiation in young adolescents rose sharply in the 1960s and peaked in the 1970s, in older adolescents it increased throughout the period, and in young adults it declined after 1970. In the period 1930-2005, 27.3% of 9,605 people aged 36-50 years who smoked ceased smoking. Rates of cessation in this age group increased throughout but decreased in males after 1990 and plateaued around 2000 in females. CONCLUSION: Our findings show substantial variation in the efficacy of tobacco control policies across age groups, with a notable lack of success among the younger population.

Smoking Cessation and Incident Atrial Fibrillation in a Longitudinal Cohort.

BACKGROUND: Although smoking heightens the risk of AF, it remains unknown if that risk is amenable to modification after smoking cessation. OBJECTIVES: This study sought to evaluate the association between smoking cessation and atrial fibrillation (AF) risk in a large longitudinal cohort. METHODS: After excluding those with prevalent AF and no history of smoking at baseline, we evaluated 146,772 UK Biobank participants with serial smoking assessments. We compared AF risk between former smokers at baseline and those who quit smoking during the study to current smokers. Incident AF was ascertained from outpatient and inpatient encounters and identified using International Classification of Diseases codes. Cox models were used to compare the risk of incident AF among current and former smokers as well as those who quit smoking during the study while controlling for age, sex, race, body mass index, education, cardiovascular comorbidities, alcohol use, and pack-years. RESULTS: Among the 146,772 participants (48.3% female; age: 57.3 ± 7.9 years), 37,377 (25.5%) currently smoked; 105,429 (72.0%) were former smokers; and 3,966 (2.7%) quit smoking during the study. Over a mean 12.7 ± 2.0 years of follow-up, 11,214 (7.6%) participants developed AF. Compared to current smokers, the adjusted risk of AF was 13% lower in former smokers (HR: 0.87; 95% CI: 0.83-0.91) and 18% lower in those who quit smoking during the study (HR: 0.82; 95% CI: 0.70-0.95). CONCLUSIONS: Compared to those who continue to smoke, smoking cessation was associated with a lower risk of AF.

Smoking cessation to prevent death and tuberculosis recurrence after treatment: A prospective cohort study with a seven-year follow-up in China.

Background: Although there is consistent evidence that smoking is a risk factor associated with tuberculosis (TB), whether smoking cessation improves treatment outcomes and reduces the risk of TB recurrence remains understudied. Methods: We conducted a prospective cohort study with a seven-year follow-up in China. We recruited newly-diagnosed TB patients and classified them as non-smokers, ex-smokers, and current smokers. Current smokers were invited to participate in a smoking cessation intervention programme. We used a Cox proportional hazards model to assess the risk of death among TB patients and the risk of recurrence among successfully treated patients. Results: In total, 634 (79.2%) patients completed anti-TB treatments and 115 (14.4%) patients died. We confirmed the existence of a dose-response relationship between smoking frequency and the risk of TB recurrence (the slope of the fitted line >0; P < 0.05). Compared to those who continued smoking, the risk of death and recurrent TB for the patients who quit smoking during treatment decreased. The HR of mortality for smokers who smoked 30 or more cigarettes was 2.943 (95% confidence interval (CI) = 1.035-8.368), while the HR of mortality for those who smoked 30 or more cigarettes, but quit during treatment was 2.117 (95% CI = 1.157-3.871). However, the risk of recurrence remained high for ex-smokers who had a smoking history of 25 years or more. Conclusions: Our study provides further evidence supporting the World Health Organization's call for co-management of smoking and other risk factors as part of routine TB treatment.

Post-diagnosis smoking habit change and incident dementia in cancer survivors.

INTRODUCTION: Many individuals change their smoking habits after cancer diagnosis. We aimed to evaluate the association of post-diagnosis smoking habit change with incident dementia in cancer survivors. METHODS: We identified 558,127 individuals who were diagnosed with cancer at age ≥ 20 and survived for ≥ 3 years. Participants were classified into four groups: (1) sustained non-smokers, (2) initiators/relapsers, (3) quitters, and (4) continuing smokers. Dementia risk in each group was assessed using a cause-specific Cox model. RESULTS: After cancer diagnosis, 2.3% of pre-diagnosis non-smokers initiated/relapsed into smoking, while 51.7% of pre-diagnosis smokers quit smoking. Compared to sustained non-smokers, multivariable-adjusted risk of dementia was 29% higher among initiators/relapsers, 11% higher among quitters, and 31% higher among continuing smokers. Compared to continuing smokers, the risk was 15% lower among quitters. DISCUSSION: In cancer survivors, smoking initiation/relapse was associated with increased risk of dementia, whereas smoking cessation was associated with decreased risk of dementia. HIGHLIGHTS: Approximately half of pre-diagnosis smokers quit smoking after a cancer diagnosis. Smoking cessation was associated with a 15% reduced risk of dementia. More than 2% of pre-diagnosis non-smokers initiated or relapsed into smoking after a cancer diagnosis. Smoking initiation/relapse was associated with a 29% elevated risk of dementia.

Smoking Cessation and Risk of Hidradenitis Suppurativa Development.

Importance: Although tobacco smoking is established as a risk factor for hidradenitis suppurativa (HS), studies on the effects of smoking cessation on HS are limited, and evidence is lacking. Objective: To examine the association between changes in smoking status and the development of HS. Design, Setting, and Participants: This population-based cohort study enrolled participants from the Korean National Health Insurance Service database who had undergone 2 consecutive biennial health examinations (2004-2005 and 2006-2007) as the primary cohort. Within the primary cohort, the secondary cohort comprised individuals who underwent all biennial health examinations throughout the follow-up period and maintained the same smoking status from 2006 to 2007 to the end of the follow-up period. Data were analyzed from July to December 2023. Exposures: Changes in smoking habit status. Main Outcomes and Measures: Risk of HS development. The HS risk according to change in smoking status between the 2 consecutive health examinations was estimated using a Cox proportional hazards model. Results: Of the 6 230 189 participants enrolled, the mean (SD) age was 47.2 (13.5) years, and 55.6% were male. During 84 457 025 person-years of follow-up, 3761 HS events occurred. In the primary cohort, compared to those who consistently reported active smoking at both checkups (ie, sustained smokers), lower HS risk was seen among those who were confirmed to smoke initially but quit by the second checkup (ie, smoking quitters) (adjusted hazard ratio [AHR], 0.68; 95% CI, 0.56-0.83), those who maintained cessation status throughout (AHR, 0.67; 95% CI, 0.57-0.77), and those who reported never smoking at either checkup (ie, never smokers) (AHR, 0.57; 95% CI, 0.52-0.63). Those who initially quit smoking but resumed by the second checkup and those who had no previous smoking history but started at the second checkup (ie, new smokers) exhibited similar HS risk as sustained smokers. The secondary cohort results aligned with those of the primary cohort, showing a more pronounced risk reduction with smoking cessation (AHR, 0.57; 95% CI, 0.39-0.83). Considering time-smoking interaction, the cumulative incidence and the risk of HS in smoking quitters were similar to those in sustained smokers in the early stages of observation. However, 3 to 4 years after smoking cessation, the rate decelerated, resembling that of never smokers, and there was a statistically significant decrease in the risk that persisted (between 3 and 6 years from the index date: AHR, 0.58; 95% CI, 0.36-0.92; and ≥12 years from the index date: AHR, 0.70; 95% CI, 0.50-0.97). New smokers initially paralleled never smokers but accelerated after 2 to 3 years, reaching sustained smokers' levels. Conclusions and Relevance: In this cohort study, quitting smoking and sustaining a smoke-free status were associated with a reduced risk of HS development compared to continuous smoking. In contrast, resuming or initiating smoking may have as detrimental an effect on HS development as continual smoking.