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Smoking prevalence in several high-income countries is steadily declining but remains persistently high in ‘lower’ socioeconomic position (SEP) groups, contributing to inequities in morbidity and mortality. Smoking to relieve stress is a commonly endorsed motive for continued smoking; however, it remains unclear whether smoking to relieve stress has a negative impact on motivation to stop and future quit attempts and if so, whether associations are moderated by SEP. This was an observational study with cross-sectional and prospective survey data from the nationally representative Smoking Toolkit Study in England. A total of 1,135 adult smokers were surveyed at baseline, with 153 (13.5%) respondents followed up at 12 months. Respondents provided information on demographic, social and smoking characteristics. A series of multivariable logistic regression analyses was conducted. Bayes Factors (BFs) were calculated to explore non-significant associations. Smoking to relieve stress was commonly endorsed by respondents from both ‘lower’ (43.2% [95% CI = 39.4%, 47.0%]) and ‘higher’ (40.5% [95% CI = 35.9%, 45.1%]) SEP groups ( p = 0.39). Smoking to relieve stress was associated with high motivation to stop at baseline (OR adj = 1.48, 95% CI = 1.03–2.12, p = 0.035) but not significantly with the odds of making a quit attempt at a 12-month follow-up, although the magnitude and direction of the effect was similar to that observed for high motivation to stop (OR adj = 1.49, 95% CI = 0.69–3.20, p = 0.3). Data were insensitive to detect moderation effects of SEP (BF = 0.90 and BF = 1.65, respectively). Smoking to relieve stress is a commonly endorsed motive and is associated with high motivation to stop but not significantly with the odds of making a quit attempt in the next 12 months, although the magnitude and direction of the effect was similar for both outcomes. There was no clear evidence of moderation by SEP, although data were insensitive to distinguish the alternative from the null hypothesis.

ObjectivesThis study assessed public support for four proposed tobacco control policies in Great Britain: (1) Raising the sales age of tobacco by 1 year every year (Smokefree Generation); (2) Raising the sales age of tobacco from 18 years to 21 years; (3) Providing prescription e-cigarettes as smoking cessation aids to adults who smoke; (4) Restricting e-cigarette advertising to prevent youth uptake.DesignRepeat cross-sectional population-based survey weighted to match the population of Great Britain.SettingThe survey was conducted in England, Scotland and Wales in September 2021, October 2022 and October 2023.Participants6541 adults living in Great Britain.Main outcome measuresSupport for each policy and year and prevalence ratios (PRs) comparing support between years and subgroups.ResultsThe most popular policy each year was restricting e-cigarette advertising (74%/79%/85%), followed by raising the sales age to 21 years (50%/58%/64%), providing prescription e-cigarettes (45%/44%/47%) and Smokefree Generation (34%/44%/49%). The largest increases were for policies about the age of sale (Smokefree Generation: 2021/2022 PR=1.28, 95% CI 1.18 to 1.40, 2022/2023 PR=1.12, 95% CI 1.04 to 1.20; raising the age to 21 years: 2021/2022 PR=1.16, 95% CI 1.09 to 1.23, 2022/2023 PR=1.11, 95% CI 1.05 to 1.17). Only 30% opposed Smokefree Generation in 2023 down from 41% in 2021.ConclusionsSupport for each policy increased each year, except for providing prescription e-cigarettes. Restricting e-cigarette advertising was the most popular policy, while support for age of sale policies, in particular for a Smokefree Generation, grew most.Trial registrationThe study protocol was published on the Open Science Framework (https://osf.io/46z2c/) prior to starting the analysis.

BackgroundIn the UK in May 2016, standardised packaging of tobacco products was implemented, including minimum pack sizes of 20 sticks or 30 g loose tobacco. The change was intended to reduce uptake by increasing upfront costs to young people, but there was concern it may unintentionally increase consumption among people smoking. This study aimed to assess whether the introduction of the policy was associated with changes in (1) mean daily factory-made (FM)/roll-your-own (RYO) cigarettes consumption among people smoking predominantly (a) FM and (b) RYO cigarettes; and (2) current smoking prevalence among 16–24-year-olds.MethodsData (N=257 929) were from a representative monthly cross-sectional survey of adults (≥16 years) in England, collected between November 2007 and January 2020. Outcome measures were mean daily (FM/RYO) cigarette consumption among those smoking FM/RYO cigarettes, and prevalence of current smoking among 16–24-year-olds. Time-series analyses were conducted using Autoregressive Integrated Moving Average with Exogenous variables (ARIMAX) regression models including a gradual level change starting in June 2017 and ending in May 2018 for cigarette consumption and a step change in June 2016 for prevalence of current smoking.ResultsThe ARIMAX model was not able to detect a change in mean daily cigarette consumption—for FM (Badj=−0.543, 95% CI −1.381 to 0.296) or RYO (Badj=0.002, 95% CI −0.518 to 0.522) following the implementation of standardised packaging. The unadjusted analysis suggested the implementation of standardised packaging was associated with a small (3%) decrease in smoking prevalence among 16–24-year-olds (Bunadj=−0.031, 95% CI −0.062 to 0.000), but this association was attenuated after adjustment for covariates (Badj=−0.010, 95% CI −0.039 to 0.019).ConclusionsThe implementation of standardised packaging of tobacco products was not associated with a meaningful change in the mean number of FM or RYO cigarettes consumed by people smoking in England, suggesting the larger pack size has not had an unintended consequence of substantially increasing cigarette consumption. However, there was also little evidence that the policy substantially reduced smoking among 16–24-year-olds.

Background Vaping prevalence has increased rapidly in England since 2021. This study estimated trends between 2013 and 2024 in vaping among ex-smokers, overall and among those who did not use e-cigarettes to support their quit attempt. Methods Data were collected via nationally-representative, monthly cross-sectional surveys in England, October 2013 to May 2024. We analysed data from 54,251 adults (≥ 18y) who reported having tried to stop smoking in the past year or having stopped smoking more than a year ago. Logistic regression estimated associations between time and e-cigarette use. Results Across the period, there were increases in the use of e-cigarettes to support attempts to stop smoking (from 26.9% [24.0–30.0%] in October 2013 to 41.4% [37.7–45.2%] in May 2024), in current vaping among ≥ 1y ex-smokers (1.9% [1.5–2.5%] to 20.4% [18.7–22.2%]), and in late uptake of vaping after smoking cessation (i.e., current vaping among people who quit smoking before e-cigarettes started to become popular in 2011; 0.4% [0.2–0.8%] to 3.7% [2.8–4.9%]). These increases were non-linear, with much of the difference occurring since mid-2021, and were greatest at younger ages (e.g., current vaping among ≥ 1y ex-smokers reached 58.9% among 18-year-olds vs. 10.7% among 65-year-olds). Conclusions Vaping prevalence increased substantially among adult ex-smokers in England over the past decade, particularly at younger ages. While this is likely to have been largely driven by increased use of e-cigarettes in quit attempts and continued use thereafter, there was also evidence of increased uptake of vaping among those who had been abstinent from smoking for many years.

Objective To identify the most commonly reviewed behaviour change techniques (BCTs) and their effectiveness based on consistency across reviews for lifestyle interventions of non-communicable diseases. Design Umbrella review of systematic reviews. Data sources PubMed, Embase, PsycINFO, Cochrane CENTRAL, Global Health. Data extraction and synthesis A narrative synthesis of extracted findings was conducted. The Behaviour Change Technique v1 Taxonomy was used to identify and code behaviour change techniques (e.g., goal setting) in a standardised manner, which were independently assessed by two reviewers. Study quality was independently assessed by two reviewers using the assessment of multiple systematic review tools. Results 26 reviews were included with a total of 72 BCT labels evaluated across the different lifestyle interventions and non-communicable diseases. A total of 13 BCT clusters were identified to be reported as effective. The most commonly reviewed BCTs and their effectiveness/ineffectiveness were as follows: ‘Goals and Planning’ (12 effective/1 ineffective), ‘Feedback and monitoring’ (9 effective/3 ineffective), ‘Social support’ (9 effective/1 ineffective), ‘Shaping knowledge’ (11 effective/1 ineffective), and ‘Natural consequences’ (6 effectiveness/ 2 ineffective). The vast majority of the studies were conducted in high-income and a few in upper middle-income countries, with hardly any studies from lower middle-income and lower income studies. Conclusion The most common BCTs were ‘Goals and Planning’, ‘Feedback and Monitoring’, ‘Shaping Knowledge’, ‘Social Support’, and ‘Natural Consequence’. Based on consistency across reviews, several BCTs such as ‘Goals and Planning’, Feedback and Monitoring’, ‘Shaping Knowledge’, and ‘Social Support’ have demonstrated effectiveness (Recommendation Grade A) in improving health behaviours across a limited range of NCDs. The evidence is less clear for other BCT techniques. It is also likely that not all BCTs will be transferable across different settings. There is a need for more research in this area, especially in low-middle-income countries. Protocol registration Registered on the International Prospective Register of Systematic Reviews; PROSPERO (CRD42020222832).

The COVID-19 pandemic led to changes in alcohol consumption in England. Evidence suggests that one-fifth to one-third of adults increased their alcohol consumption, while a similar proportion reported consuming less. Heavier drinkers increased their consumption the most and there was a 20% increase in alcohol-specific deaths in England in 2020 compared with 2019, a trend continuing through 2021 and 2022. This study aimed to quantify future health, healthcare, and economic impacts of changes in alcohol consumption observed during the COVID-19 pandemic. This study used a validated microsimulation model of alcohol consumption and health outcomes. Inputted data were obtained from the Alcohol Toolkit Study, and demographic, health and cost data from published literature and publicly available datasets. Three scenarios were modelled: short, medium, and long-term, where 2020 drinking patterns continue until the end of 2022, 2024, and 2035, respectively. Disease incidence, mortality, and healthcare costs were modelled for nine alcohol-related health conditions. The model was run from 2020 to 2035 for the population of England and different occupational social grade groups. In all scenarios, the microsimulation projected significant increases in incident cases of disease, premature mortality, and healthcare costs, compared with the continuation of pre-COVID-19 trends. If COVID-19 drinking patterns continue to 2035, we projected 147,892 excess cases of diseases, 9,914 additional premature deaths, and £1.2 billion in excess healthcare costs in England. The projections show that the more disadvantaged (C2DE) occupational social grade groups will experience 36% more excess premature mortality than the least disadvantaged social group (ABC1) under the long-term scenario. Alcohol harm is projected to worsen as an indirect result of the COVID-19 pandemic and inequalities are projected to widen. Early real-world data corroborate the findings of the modelling study. Increased rates of alcohol harm and healthcare costs are not inevitable but evidence-based policies and interventions are required to reverse the impacts of the pandemic on alcohol consumption in England.

Background Smoking has played a significant role in the historical culture of mental healthcare settings. Mental health professionals (MHPs) often hold dismissive attitudes regarding the importance of smoking cessation in the context of mental healthcare. In 2007, English mental health inpatient buildings were required by law to become smoke-free, and healthcare trusts have more recently begun to implement comprehensive policies (i.e. smoke-free grounds and buildings) and staff training in response to national guidance. It is therefore important to explore MHPs practice around smoking, smoking cessation, and smoke-free policy adherence. This study aimed to explore these issues by using the COM-B (capability, opportunity, motivation, behaviour) model to systematically identify barriers to, and facilitators for, MHPs addressing smoking with their patients. Methods Five focus groups with a total of 36 MHPs were conducted between March and August 2017. MHPs were recruited from one of the largest mental health trusts in Europe. Discussions were guided by a semi-structured guide. Responses were audio recorded, transcribed and coded using thematic analysis and the COM-B framework. Results Addressing smoking with patients was undermined by MHPs’ 1) psychological capability to recall training content, misunderstand the potential benefits of addressing patient smoking and harm reduction approaches; 2) physical opportunity in terms of time constraints, and easy accessibility of tobacco in the community; 3) social opportunity in terms of increased cultural value of tobacco following inpatient smoke-free policy implementation, and lack of support from colleagues to enforce the smoke-free policy; 4) automatic motivation, including intrinsic biases regarding patients abilities and motivations to quit, and 5) reflective motivation, including perceived job role and decision making processes related to addressing behaviours deemed more important than smoking. The main facilitating factors identified were MHPs’ having opportunity in the form of patients asking directly for support, and MHPs having access to resources such as stop smoking services and spirometers. Conclusion Multiple barriers were identified across all key domains of the COM-B framework that undermine MHPs’ practice regarding smoking cessation. Few facilitators were identified which may have implications for future smoke-free policy and clinical practice.

Background Rates of diseases and death from tobacco smoking are substantially higher among those with a mental health condition (MHC). Vaping can help some people quit smoking, but little is known about vaping among people with MHCs or psychological distress. We assessed the prevalence and characteristics (heaviness, product type) of smoking and/or vaping among those with and without a history of single or multiple MHC diagnoses and with no, moderate or serious psychological distress. Methods Data from 27,437 adults in Great Britain surveyed between 2020 and 2022. Multinomial regressions analysed associations between smoking, vaping and dual use prevalence, smoking/vaping characteristics and (a) history of a single or multiple MHC and (b) moderate or serious psychological distress, adjusted for age, gender, and socioeconomic status. Results Compared with people who had never smoked, those who currently smoked were more likely to report a history of a single (12.5% vs 15.0%, AOR=1.62, 95% CI=1.46–1.81, p <.001) or multiple MHCs (12.8% vs 29.3%, AOR=2.51, 95% CI=2.28–2.75, p <.001). Compared with non-vapers, current vapers were more likely to report a history of a single (13.5% vs 15.5%, AOR=1.28, 95% CI=1.11–1.48, p <.001) or multiple MHCs (15.5% vs 33.4%, AOR=1.66, 95% CI=1.47–1.87, p <.001). Dual users were more likely to report a history of multiple MHCs (36.8%), but not a single MHC than exclusive smokers (27.2%) and exclusive vapers (30.4%) (all p <.05). Similar associations were reported for those with moderate or serious psychological distress. Smoking roll-your-own cigarettes and smoking more heavily, were associated with a history of single or multiple MHCs. There were no associations between vaping characteristics and a history of MHCs. Frequency of vaping, device type and nicotine concentration differed by psychological distress. Conclusions Smoking, vaping and dual use were substantially higher among those with a history of MHC, especially multiple MHC, and experiencing past month distress than those not having a history of MHC or experiencing past month distress respectively. Analysis used descriptive epidemiology and causation cannot be determined.

Background Following the onset of the COVID-19 pandemic, in March 2020 health care delivery underwent considerable changes. It is unclear how this may have affected the delivery of Brief Interventions (BIs) for smoking and alcohol. We examined the impact of the COVID-19 pandemic on the receipt of BIs for smoking and alcohol in primary care in England and whether certain priority groups (e.g., less advantaged socioeconomic positions, or a history of a mental health condition) were differentially affected. Methods We used nationally representative data from a monthly cross-sectional survey in England between 03/2014 and 06/2022. Monthly trends in the receipt of BIs for smoking and alcohol were examined using generalised additive models among adults who smoked in the past-year (weighted N  = 31,390) and those using alcohol at increasing and higher risk levels (AUDIT score 3 8, weighted N  = 22,386), respectively. Interactions were tested between social grade and the change in slope after the onset of the COVID-19 pandemic, and results reported stratified by social grade. Further logistic regression models assessed whether changes in the of receipt of BIs for smoking and alcohol, respectively, from 12/2016 to 01/2017 and 10/2020 to 06/2022 (or 03/2022 in the case of BIs for alcohol), depended on history of a mental health condition. Results The receipt of smoking BIs declined from an average prevalence of 31.8% (95%CI 29.4–35.0) pre-March 2020 to 24.4% (95%CI 23.5–25.4) post-March 2020. The best-fitting model found that after March 2020 there was a 12-month decline before stabilising by June 2022 in social grade ABC1 at a lower level (~ 20%) and rebounding among social grade C2DE (~ 27%). Receipt of BIs for alcohol was low (overall: 4.1%, 95%CI 3.9–4.4) and the prevalence was similar pre- and post-March 2020. Conclusions The receipt of BIs for smoking declined following March 2020 but rebounded among priority socioeconomic groups of people who smoked. BIs for alcohol among those who use alcohol at increasing and higher risk levels were low and there was no appreciable change over time. Maintaining higher BI delivery among socioeconomic and mental health priority groups of smokers and increasing and higher risk alcohol users is important to support reductions in smoking and alcohol related inequalities.

ObjectivesGambling is associated with cigarette smoking and alcohol consumption. We explored the intersection of gambling across all risk levels of harm with smoking and alcohol use among adults in Great Britain.DesignA nationally representative cross-sectional survey in October 2022.SettingGreat Britain.ParticipantsA weighted total of 2398 adults (18+ years).Outcome measuresWe examined the prevalence of past-year gambling and, among those reporting gambling, assessed the associations between the outcome of any risk of harm from gambling (scoring >0 on the Problem Gambling Severity Index) and the binary predictor variables of current cigarette smoking and higher risk alcohol consumption (AUDIT-C score≥4). We also explored data on weekly expenditure on gambling with smoking and alcohol use among those categorised at any-risk of harm from gambling.ResultsOverall, 43.6% (95% CI 41.2% to 45.9%) of adults gambled in the past year. Among these, 7.3% (95% CI 5.3% to 9.3%) were classified at any-risk of harm from gambling, 16.0% (95% CI 13.2% to 18.8%) were currently smoking and 40.8% (95% CI 37.2% to 44.4%) were drinking at increasing and higher risk levels. There were no associations between any risk of harm from gambling and current smoking (OR adjusted=0.80, 95% CI 0.35 to 1.66) or drinking at increasing and higher risk levels (OR adjusted=0.94, 95% CI 0.52 to 1.69), respectively. Analyses using Bayes factors indicated that these data were insensitive to distinguish no effect from a range of associations (OR=95% CI 0.5 to 1.9). The mean weekly spend on gambling was £7.69 (95% CI £5.17 to £10.21) overall, £4.80 (95% CI £4.18 to £5.43) among those classified as at no risk and £45.68 (95% CI £12.07 to £79.29) among those at any risk of harm from gambling.ConclusionsPilot data in a population-level survey on smoking and alcohol use yielded similar estimates to other population-level surveys on gambling participation and at-risk gambling. Further data are needed to elucidate the intersections more reliably between gambling, smoking and alcohol use and inform population-level approaches to reduce harm.