Associations of Prevalence of E-cigarette Use With Quit Attempts, Quit Success, Use of Smoking Cessation Medication, and the Overall Quit Rate Among Smokers in England: A Time-Series Analysis of Population Trends 2007-2022.

INTRODUCTION: This study aimed to (1) provide up-to-date estimates of how changes in the prevalence of e-cigarette use have been associated with changes in smoking cessation activities and use of licensed treatments among smokers in England and (2) explore any changes in these associations over time. METHODS: Data were aggregated quarterly on 67 548 past-year smokers between Q1-2007 and Q4-2022. Explanatory variables were the prevalence of (1) current e-cigarette use among smokers and (2) e-cigarette use during a quit attempt. Outcomes were rates of quit attempts and overall quits among past-year smokers, and the quit success rate and use of licensed treatments among those who made a quit attempt. RESULTS: The success rate of quit attempts increased by 0.040% (95% CI 0.019; 0.062) for every 1% increase in the prevalence of e-cigarette use during a quit attempt. No clear evidence was found for an association between current e-cigarette use and the quit attempt rate (Badj = 0.008 [95% CI -0.045; 0.061]) or overall quit rate (Badj = 0.063 [-0.031; 0.158]); or between use of e-cigarettes during a quit attempt and the overall quit rate (Badj = 0.030 [-0.054; 0.114]), use of prescription medication (varenicline/bupropion/nicotine replacement therapy [NRT]: Badj = -0.036 [-0.175; 0.102]), or use of over-the-counter NRT (Badj = -0.052 [-0.120; 0.015]). There was no clear evidence this pattern of associations has changed substantially over time. CONCLUSIONS: Changes in the prevalence of e-cigarette use in England through 2022 have been positively associated with the success rate of quit attempts but not clearly associated with the quit attempt rate, overall quit rate, or use of licensed smoking cessation treatments. IMPLICATIONS: If the association between the increase in e-cigarette use and the quit success rate is causal, then the use of e-cigarettes in quit attempts has helped in the region of 30 000 to 50 000 additional smokers in England to successfully quit each year since they became popular in 2013, over and above the number who were quitting before the advent of e-cigarettes.

Evaluation of a regional tobacco control programme (Greater Manchester's Making Smoking History) on quitting and smoking in England 2014 to 2022: a time-series analysis.

INTRODUCTION: This study aimed to assess the impact of Greater Manchester's Making Smoking History programme - a region-wide smoking cessation programme launched in January 2018 - on key smoking and quitting outcomes. METHODS: Data were from a nationally-representative monthly survey, 2014-2022 (n=171,281). We used interrupted time-series analyses (Autoregressive Integrated Moving Average [ARIMA] and generalised additive models [GAM]) to examine regional differences between Greater Manchester and the rest of England, before and during the programme's first five years. Outcomes were rates of quit attempts and overall quits among smokers, quit success rates among smokers who tried to quit (pre-registered outcomes), and current smoking prevalence among adults (unregistered outcome). RESULTS: Results showed mixed effects of the programme on quitting. Primary ARIMA models showed comparative reductions in quit success rates (change in quarterly difference between regions = -11.03%; 95%CI -18.96;-3.11) and overall quit rates in Greater Manchester compared with the rest of England (-2.56%; 95%CI -4.95;-0.18), and no significant change in the difference in the quit attempt rate (+2.95%; 95%CI -11.64;17.54). These results were not consistently observed across sensitivity analyses or GAM analyses. Exploratory ARIMA models consistently showed smoking prevalence in Greater Manchester declined more quickly than in the rest of England following initiation of the programme (-2.14%; 95%CI -4.02;-0.27). CONCLUSIONS: The first five years of Greater Manchester's Making Smoking History programme did not appear to be associated with substantial increases in quitting activity. However, exploratory analyses showed a significant reduction in the regional smoking rate, over and above changes in the rest of England over the same period. IMPLICATIONS: Taken together, these results show a relative decline in smoking prevalence in Greater Manchester but equivocal data on quitting, introducing some uncertainty. It is possible the programme has reduced smoking prevalence in the absence of any substantial change in quitting activity by changing norms around smoking and reducing uptake, or by reducing the rate of late relapse. It is also possible that an undetected effect on quitting outcomes has still contributed to the programme's impact on reducing prevalence to some degree. It will be important to evaluate the overall impact of the programme over a longer timeframe.

Association between the implementation of standardised tobacco packaging legislation and illicit tobacco and cross-border purchasing in England: a time-series analysis between 2012 and 2020.

BACKGROUND: In May 2016, the UK announced standardising packaging legislation for tobacco products. There was a 12-month transition period with both branded and standardised packs on the market until May 2017. The aim of this study was to investigate whether the implementation of standardised packaging in England was associated with changes in illicit tobacco and cross-border purchasing. METHODS: We used Smoking Toolkit Study data covering the time period from 2012 to 2020. We ran time-series analysis using Autoregressive Integrated Moving Average with Exogenous Variable models to investigate the monthly changes in illicit tobacco and cross-border purchasing in England. The model was adjusted for other tobacco control policies implemented during the relevant time period and tobacco pricing. We used May 2017 as an implementation point and run sensitivity analysis using July 2016 and February 2017 as alternative implementation points given phased introduction of the policy. RESULTS: The average prevalence of illicit tobacco and cross-border purchasing in the past 6 months was 14.4%. The implementation of standardised tobacco packaging legislation was associated with a monthly decline in illicit tobacco and cross-border purchases after May 2017 by 0.16% per month (beta=-0.158, 95% CI -0.270 to -0.046). The results were robust to considering different implementation points for the policy (July 2016: beta=-0.109, 95% CI -0.213 to -0.005; February 2017: beta=-0.141, 95% CI -0.245 to -0.036). CONCLUSIONS: In contrast to the tobacco industry's argument that the legislation would lead to an increase in the illicit tobacco and cross-border market, this study demonstrates that the implementation of the policy is associated with a decline in illicit tobacco and cross-border purchases in England.

Impact of Standardised Packaging of Tobacco Products Regulations on cigarette consumption and youth smoking in England: interrupted time-series analysis.

BACKGROUND: In 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. METHODS: Data (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. RESULTS: The 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). CONCLUSIONS: The 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.

Evaluating the Acceptability of the Drink Less App and the National Health Service Alcohol Advice Web Page: Qualitative Interview Process Evaluation.

BACKGROUND: The extent to which interventions are perceived as acceptable to users impacts engagement and efficacy. OBJECTIVE: In this study, we evaluated the acceptability of (1) the smartphone app Drink Less (intervention) and (2) the National Health Service (NHS) alcohol advice web page (usual digital care and comparator) among adult drinkers in the United Kingdom participating in a randomized controlled trial evaluating the effectiveness of the Drink Less app. METHODS: A subsample of 26 increasing- and higher-risk drinkers (Alcohol Use Disorders Identification Test score≥8) assigned to the intervention group (Drink Less; n=14, 54%; female: n=10, 71%; age: 22-72 years; White: n=9, 64%) or usual digital care group (NHS alcohol advice web page; n=12, 46%; female: n=5, 42%; age: 23-68 years: White: n=9, 75%) took part in semistructured interviews. The interview questions were mapped on to the 7 facets of acceptability according to the Theoretical Framework of Acceptability: affective attitude, burden, perceived effectiveness, ethicality, intervention coherence, opportunity costs, and self-efficacy. Alongside these constructs, we also included a question on perceived personal relevance, which previous research has linked to acceptability and engagement. Framework and thematic analysis of data was undertaken. RESULTS: The Drink Less app was perceived as being ethical, easy, user-friendly, and effective for the period the app was used. Participants reported particularly liking the tracking and feedback sections of the app, which they reported increased personal relevance and which resulted in positive affect when achieving their goals. They reported no opportunity cost. Factors such as negative affect when not meeting goals and boredom led to disengagement in the longer term for some participants. The NHS alcohol advice web page was rated as being easy and user-friendly with no opportunity costs. However, the information presented was not perceived as being personally relevant or effective in changing drinking behavior. Most participants reported neutral or negative affect, most participants thought the alcohol advice web page was accessible, and some participants reported ethical concerns around the availability of suggested resources. Some participants reported that it had acted as a starting point or a signpost to other resources. Participants in both groups discussed motivation to change and contextual factors such as COVID-19 lockdowns, which influenced their perceived self-efficacy regardless of their assigned intervention. CONCLUSIONS: Drink Less appears to be an acceptable digital intervention among the recruited sample. The NHS alcohol advice web page was generally considered unacceptable as a stand-alone intervention among the recruited sample, although it may signpost and help people access other resources and interventions.

Have there been sustained impacts of the COVID-19 pandemic on trends in smoking prevalence, uptake, quitting, use of treatment, and relapse? A monthly population study in England, 2017-2022.

BACKGROUND: Studies conducted during the early stages of the pandemic documented mixed changes in smoking behaviour: more smokers quitting successfully but little change in prevalence. This study aimed to examine whether there have been sustained impacts of the COVID-19 pandemic on smoking patterns in England. METHODS: Data were from 101,960 adults (≥ 18 years) participating in the Smoking Toolkit Study, a monthly representative household survey, between June 2017 and August 2022. Interviews were conducted face-to-face until March 2020 and via telephone thereafter. Generalised additive models estimated associations of the pandemic onset (March 2020) with current smoking, uptake, cessation, quit attempts, and use of support. Models adjusted for seasonality, sociodemographic characteristics, and (where relevant) dependence and tobacco control mass-media expenditure. RESULTS: Before the COVID-19 pandemic, smoking prevalence fell by 5.2% per year; this rate of decline slowed to 0.3% per year during the pandemic (RRΔtrend = 1.06, 95% CI = 1.02, 1.09). This slowing was evident in more but not less advantaged social grades (RRΔtrend = 1.15, 1.08, 1.21; RRΔtrend = 1.00, 0.96, 1.05). There were sustained step-level changes in different age groups: a 34.9% (95% CI = 17.7, 54.7%) increase in smoking prevalence among 18-24-year-olds, indicating a potential rise in uptake, in contrast to a 13.6% (95% CI = 4.4, 21.9%) decrease among 45-65-year-olds. In both age groups, these step-level changes were followed by the pre-pandemic declines stopping, and prevalence remaining flat. There were sustained increases in quitting among past-year smokers, with a 120.4% (95% CI = 79.4, 170.9%) step-level increase in cessation and a 41.7% (95% CI = 29.7, 54.7%) increase in quit attempts. The main limitation was the change in modality of data collection when the pandemic started; while this may have contributed to the step-level changes we observed, it is unlikely to explain changes in the slope of trends. CONCLUSIONS: In England, the rate of decline in adult smoking prevalence stagnated during the COVID-19 pandemic through to 2022. At the start of the pandemic, a potential reduction in smoking prevalence among middle-aged adults and increases in quitting among smokers may have been offset by an increase in smoking among young adults. The slowing in the rate of decline was pronounced in more advantaged social grades.

E-cigarettes to Augment Stop Smoking In-person Support and Treatment With Varenicline (E-ASSIST): A Pragmatic Randomized Controlled Trial.

AIM: To examine whether, in adults receiving behavioral support, offering e-cigarettes together with varenicline helps more people stop smoking cigarettes than varenicline alone. METHODS: A two-group, parallel arm, pragmatic randomized controlled trial was conducted in six English stop smoking services from 2019-2020. Adults enrolled onto a 12-week programme of in-person one-to-one behavioral smoking cessation support (N  =  92) were randomized to receive either (1) a nicotine e-cigarette starter kit alongside varenicline or (2) varenicline alone. The primary outcome was biochemically verified abstinence from cigarette smoking between weeks 9-to-12 post quit date, with those lost to follow-up considered not abstinent. The trial was stopped early due to COVID-19 restrictions and a varenicline recall (92/1266 participants used). RESULTS: Nine-to-12-week smoking abstinence rates were 47.9% (23/48) in the e-cigarette-varenicline group compared with 31.8% (14/44) in the varenicline-only group, a 51% increase in abstinence among those offered e-cigarettes; however, the confidence interval (CI) was wide, including the possibility of no difference (risk ratio [RR] = 1.51, 95% CI = 0.91-2.64). The e-cigarette-varenicline group had 43% lower hazards of relapse from continuous abstinence than the varenicline-only group (hazards ratio [HR] = 0.57, 95% CI =  0.34-0.96). Attendance for 12 weeks was higher in the e-cigarette-varenicline than varenicline-only group (54.2% vs. 36.4%; RR = 1.49, 95% CI = 0.95-2.47), but similar proportions of participants in both groups used varenicline daily for ≥8 weeks after quitting (22.9% versus 22.7%; RR = 1.01, 95% CI = 0.47-2.20). Estimates were too imprecise to determine how adverse events differed by group. CONCLUSION: Tentative evidence suggests that offering e-cigarettes alongside varenicline to people receiving behavioral support may be more effective for smoking cessation than varenicline alone. IMPLICATIONS: Offering e-cigarettes to people quitting smoking with varenicline may help them remain abstinent from cigarettes, but the evidence is tentative because our sample size was smaller than planned-caused by Coronavirus Disease 2019 (COVID-19) restrictions and a manufacturing recall. This meant our effect estimates were imprecise, and additional evidence is needed to confirm that providing e-cigarettes and varenicline together helps more people remain abstinent than varenicline alone.

Patterns and predictors of adherence to health-protective measures during COVID-19 pandemic in the UK: cross-sectional and longitudinal findings from the HEBECO study.

BACKGROUND: Adherence to health-protective behaviours (regularly washing hands, wearing masks indoors, maintaining physical distancing, carrying disinfectant) remains paramount for the successful control of COVID-19 at population level. It is therefore important to monitor adherence and to identify factors associated with it. This study assessed: 1) rates of adherence, to key COVID-19 health-protective behaviours and 2) the socio-demographic, health and COVID-19-related factors associated with adherence. METHODS: Data were collected on a sample of UK-based adults during August-September 2020 (n = 1,969; lockdown restrictions were eased in the UK; period 1) and November 2020- January 2021 (n = 1944; second UK lockdown; period 2). RESULTS: Adherence ranged between 50-95%, with higher adherence during the period of stricter measures. Highest adherence was observed for wearing masks indoors (period 1: 80.2%, 95%CI 78.4%-82.0%, period 2: 92.4%, 95%CI 91.1%-93.6%) and lowest for carrying own disinfectant (period 1: 48.4%, 95%CI 46.2%-50.7%, period 2: 50.7%, 95%CI 48.4%-53.0%). Generalized estimating equation models indicated that key factors of greater odds of adherence included being female, older age, having higher income, residing in England, living with vulnerable individuals and perceived high risk of COVID-19. CONCLUSIONS: Targeted messages to different demographic groups may enhance adherence to health-protective behaviours, which is paramount for the control of airborne respiratory diseases. PROTOCOL AND ANALYSIS PLAN REGISTRATION: The analysis plan was pre-registered, and it is available at https://osf.io/6tnc9/ .

Smokers' Use of E-Cigarettes in Situations Where Smoking Is not Permitted in England: Quarterly Trends 2011-2020 and Associations With Sociodemographic and Smoking Characteristics.

INTRODUCTION: This study aimed to examine how the proportion of dual users of cigarettes and e-cigarettes who use e-cigarettes in situations where smoking is not permitted has changed since e-cigarettes became popular in England, and to characterize those who do so. METHODS: Data were from 5081 adults in England who reported current smoking and current use of e-cigarettes ('dual users') participating in a nationally-representative monthly survey between April 2011 and February 2020. We modeled quarterly changes in prevalence of e-cigarette use in situations where smoking is not permitted and assessed multivariable associations with sociodemographic and smoking characteristics. RESULTS: Between 2011 and 2020, prevalence of e-cigarette use in situations where smoking is not permitted followed a positive cubic trend, with a decelerating increase from an estimated 52.5% of dual users in Q2-2011 to 72.7% in Q3-2014, followed by a small decline to 67.5% in 2018, and subsequent increase to 74.0% in 2020. Odds were higher among those who were from more disadvantaged social grades, reported stronger smoking urges, or had made a past-year quit attempt, and lower among those who were aged at least 65 years (vs. 16-24 years), from the south (vs. north) of England, reported currently cutting down on their cigarette consumption or currently using nicotine replacement therapy. CONCLUSIONS: In England, use of e-cigarettes in situations where smoking is not permitted is common among dual cigarette and e-cigarette users, has increased nonlinearly since 2011, and is particularly prevalent among those who are younger, disadvantaged, more addicted, have recently failed to quit, and are not attempting to cut down. IMPLICATIONS: The ability to use nicotine in smoke-free settings appears to be an important part of the appeal of e-cigarettes. It is possible that if e-cigarette use was prohibited in public places, smokers may be deterred from using e-cigarettes alongside combustible tobacco, which could undermine quitting. Our results suggest disadvantaged and more addicted smokers would be disproportionately affected, suggesting such restrictions may contribute to inequalities in smoking and associated health outcomes.

Independent Associations Between Different Measures of Socioeconomic Position and Smoking Status: A Cross-Sectional Study of Adults in England.

INTRODUCTION: To gain a better understanding of the complex and independent associations between different measures of socioeconomic position (SEP) and smoking in England. AIMS AND METHODS: Between March 2013 and January 2019 data were collected from 120 496 adults aged 16+ in England taking part in the Smoking Toolkit Study. Of these, 18.04% (n = 21 720) were current smokers. Six indicators of SEP were measured: social grade, employment status, educational qualifications, home and car ownership and income. Models were constructed using ridge regression to assess the contribution of each measure of SEP, taking account of high collinearity. RESULTS: The strongest predictor of smoking status was housing tenure. Those who did not own their own home had twice the odds of smoking compared with homeowners (odds ratio [OR] = 2.01). Social grade, educational qualification, and income were also good predictors. Those in social grades C1 (OR = 1.04), C2 (OR = 1.29), D (OR = 1.39), and E (OR = 1.78) had higher odds of smoking than those in social grade AB. Similarly, those with A-level/equivalent (OR = 1.15), GCSE/vocational (OR = 1.48), other/still studying (OR = 1.12), and no post-16 qualifications (OR = 1.48) had higher odds of smoking than those with university qualifications, as did those who earned in the lowest (OR = 1.23), third (OR = 1.18), and second quartiles (OR = 1.06) compared with those earning in the highest. Associations between smoking and employment (OR = 1.03) and car ownership (OR = 1.05) were much smaller. CONCLUSIONS: Of a variety of socioeconomic measures, housing tenure appears to be the strongest independent predictor of smoking in England, followed by social grade, educational qualifications, and income. Employment status and car ownership have the lowest predictive power. IMPLICATIONS: This study used ridge regression, a technique which takes into account high collinearity between variables, to gain a better understanding of the independent associations between different measures of SEP and smoking in England. The findings provide guidance as to which SEP measures one could use when trying to identifying individuals most at risk from smoking, with housing tenure identified as the strongest independent predictor.

Association of initial e-cigarette and other tobacco product use with subsequent cigarette smoking in adolescents: a cross-sectional, matched control study.

INTRODUCTION: This study assessed whether initiating e-cigarette use increases the uptake of cigarette smoking in US adolescents compared with behavioural and synthetic controls. METHODS: Data come from 78 265 adolescents in the National Youth Tobacco Survey (2014-2017) of whom 38 630 provided information about the first tobacco product they had used in 2014/15. Ever, past 30 day and established (30 day use and 100+ lifetime cigarettes) cigarette smoking was compared in adolescents who first used an e-cigarette (exposure group), a non-cigarette combustible (CT) or other non-combustible tobacco (NT) product (behavioural controls), and propensity score matched adolescents without initial e-cigarette use (synthetic controls). RESULTS: Relative to behavioural controls, adolescents who tried e-cigarettes first were less likely to have ever smoked cigarettes (26% vs CT (42.4%; OR 0.48, 95% CI 0.40 to 0.57), or NT initiators (52.7%; OR 0.32, 95% CI 0.26 to 0.39)), to be past 30 day (6% vs CT (11.9%; OR 0.48, 95% CI 0.36 to 0.62), or NT initiators (20.0%; OR 0.26, 95% CI 0.19 to 0.35)) or be established cigarette smokers (0.7% vs CT (3.9%; OR 0.17, 95% CI 0.10 to 0.30), or NT initiators (8.4%; OR 0.08, 95% CI 0.04 to 0.13)). E-cigarette initiators were also less likely than synthetic controls (without initial e-cigarette use) to have ever smoked cigarettes (OR 0.76, 95% CI 0.62 to 0.93), be past 30 day (OR 0.71, 95% CI 0.55 to 0.91) or be established cigarette smokers (OR 0.26, 95% CI 0.13 to 0.51). CONCLUSION: Less than 1% of US adolescents who use e-cigarettes first were established cigarette smokers. They were less likely to be smokers than adolescents who tried other combustible or non-combustible tobacco products first and propensity score matched adolescents without initial e-cigarette use.

Was the implementation of standardised tobacco packaging legislation in England associated with changes in smoking prevalence? A segmented regression analysis between 2006 and 2019.

BACKGROUND AND AIM: In 2016, England initiated the implementation of standardised tobacco packaging, introduced in conjunction with minimum pack sizes and other measures included in the 2014 European Tobacco Products Directive, over the course of a 1-year sell-off period ending in May 2017. These measures have been shown to have been associated with increases in tobacco prices and product diversity. We now investigate the association between implementation of the new legislation and smoking status in England. DESIGN: Segmented regression analysis of repeated cross-sectional surveys using a generalised linear model with individual-level data to test for a change in trend and immediate step change. SETTING: England. PARTICIPANTS: Participants in the Smoking Toolkit Study, which involves repeated, cross-sectional household surveys of individuals aged 16 years and older in England. The sample included 278 219 individual observations collected between November 2006 and December 2019. INTERVENTION: Implementation of standardised packaging legislation (May 2016 and May 2017). MEASUREMENTS: Individual-level current smoking status adjusted for implementation of tobacco control policies, cigarette price, seasonality and autocorrelation. FINDINGS: The implementation of standardised packaging was associated with a significant step reduction in the odds of being a smoker after May 2017 (OR: 0.93; 95% CI 0.87 to 0.99). The magnitude of the association was similar when modelling the step change in May 2016 at the start of the 1-year policy implementation period (OR: 0.90; 95% CI: 0.83 to 0.97). CONCLUSIONS: This is the first independent study demonstrating that implementation of standardised packaging was associated with a reduction in smoking in England which occurred in anticipation of, rather than after, full policy implementation. It appears that the odds of being a smoker were affected by the prospect of the move to standardised packs and accompanying legislation.

The impact of celebrity influence and national media coverage on users of an alcohol reduction app: a natural experiment.

BACKGROUND: Smartphone apps are increasingly used for health-related behaviour change and people discover apps through different sources. However, it is unclear whether users differ by mode of app discovery. Drink Less is an alcohol reduction app that received national media coverage in the UK caused by celebrity influence (a male TV and radio national broadcaster, aged 51). Our aim was to compare users who discovered the app before and after this coverage. METHODS: A natural experiment assessing the impact of media coverage of Drink Less on users' socio-demographic and drinking characteristics, app engagement levels, and extent of alcohol reduction. The study period was from 17th May 2017 to 23rd January 2019, with media coverage starting on 21st August 2018. Users were 18 years or over, based in the UK and interested in drinking less. Interrupted time series analyses using Generalised Additive Mixed Models were conducted for each outcome variable aggregated at the weekly level. RESULTS: In 66 weeks prior to the media coverage, 8617 users downloaded the app and 18,959 in 23 weeks afterwards. There was a significant step-level increase in users' mean age (B = 8.17, p < .001) and a decrease in the percentage of female users (B = -27.71, p < .001), though these effects dissipated non-linearly over time. No effect of media coverage was detected on employment type or on the percentage of at-risk drinkers, though the mean Alcohol Use Disorders Identification Test score was lower after the media coverage (B = -1.43, p = .031). There was a step-level increase in app engagement - number of sessions (B = 3.45, p = .038) and number of days used (B = 2.30, p = .005) - which continued to increase over time following quadratic trends. CONCLUSIONS: Celebrity influence leading to national media coverage in the UK of the Drink Less app was associated with more people downloading the app who were male, older and engaged with the app; and did not appear to impact employment inequality.

Association between cigarette smoking status and voting intentions: Cross sectional surveys in England 2015-2020.

BACKGROUND AND AIMS: Cigarette smoking takes place within a cultural and social context. Political views and practices are an important part of that context. To gain a better understanding of smoking, it may be helpful to understand its association with voting patterns as an expression of the political views and practices of the population who smoke. This study aimed to assess the association between cigarette smoking and voting intentions and to examine how far any association can be explained by sociodemographic factors and alcohol use. METHODS: Pooled monthly representative repeat cross-sectional household surveys of adults (16+) in England (N = 55,482) between 2015 and 2020 were used to assess the association between cigarette smoking status and voting intentions, and whether this was accounted for by age, occupational grade, gender, region and alcohol use. Voting intention was measured by asking 'How would you vote if there were a General Election tomorrow?' Respondents chose from a list of the major English political parties or indicated their intention not to vote. RESULTS: In adjusted multinomial regression, compared with intending to vote Conservative (majority party of government during the period), being undecided (aOR1.22 [1.13-1.33] <0.001), intending to vote Labour (aOR1.27 [1.16-1.36] <0.001), to vote "Other" (aOR1.54 [1.37-1.72] <0.001), or not to vote (aOR1.93 [1.77-2.11] <0.001) was associated with higher odds of current relative to never smoking rates. Intending to vote for the Liberal Democrats was associated with a significant lower odds of current smoking prevalence (aOR0.80 [0.70-0.91] <0.001) compared with intending to vote Conservative. CONCLUSIONS: Controlling for a range of other factors, current as compared with never-smokers appear more likely to intend not to vote, to be undecided, to vote for Labour or a non-mainstream party, and less likely to vote for the Liberal Democrats, compared with the Conservative party.

Association between changes in harm perceptions and e-cigarette use among current tobacco smokers in England: a time series analysis.

BACKGROUND: There is a decreasing trend in the proportion of individuals who perceive e-cigarettes to be less harmful than conventional cigarettes across the UK, Europe and the US. It is important to assess whether this may influence the use of e-cigarettes. We aimed to estimate, using a time series approach, whether changes in harm perceptions among current tobacco smokers have been associated with changes in the prevalence of e-cigarette use in England, with and without stratification by age, sex and social grade. METHODS: Respondents were from the Smoking Toolkit Study, which involves monthly cross-sectional household surveys of individuals aged 16+ years in England. Data were aggregated monthly on ~ 300 current tobacco smokers between 2014 and 2019. The outcome variable was the prevalence of e-cigarette use. The explanatory variable was the proportion of smokers who endorsed the belief that e-cigarettes are less harmful than combustible cigarettes. Covariates were cigarette (vs. non-cigarette combustible) current smoking prevalence, past-year quit attempt prevalence and national smoking mass media expenditure. Unadjusted and adjusted autoregressive integrated moving average with exogeneous variables (ARIMAX) models were fitted. RESULTS: For every 1% decrease in the mean prevalence of current tobacco smokers who endorsed the belief that e-cigarettes are less harmful than combustible cigarettes, the mean prevalence of e-cigarette use decreased by 0.48% (ßadj = 0.48, 95% CI = 0.25-0.71, p < .001). Marginal age and sex differences were observed, whereby significant associations were observed in older (but not in young) adults and in men (but not in women). No differences by social grade were detected. CONCLUSIONS: Between 2014 and 2019 in England, at the population level, monthly changes in the prevalence of accurate harm perceptions among current tobacco smokers were strongly associated with changes in e-cigarette use.

Long-term evaluation of the rise in legal age-of-sale of cigarettes from 16 to 18 in England: a trend analysis.

OBJECTIVE: To assess the long-term impact of the increase in age-of-sale of cigarettes from 16 to 18 in England in October 2007. METHODS: Data were collected between November 2006 and September 2018 on 252,601 participants taking part in a nationally representative survey of adults aged 16+ in England, the Smoking Toolkit Study (STS). We assessed the impact of the introduction of the increase in age-of-sale on prevalence of ever smoking, current smoking, and quit attempts, among 16-17 year olds compared with 18-24 year olds. RESULTS: Following the increase in age-of-sale, there was a declining trend in ever smoking that was greater among 16-17 year olds than 18-24 year olds (OR 0.990 versus OR 0.993; p = 0.019). Data on current smoking were insensitive to detect a difference between the age groups in a step-level change or change in trend following the increase in age-of-sale (Bayes factors (BFs) 0.75 and 2.10). Data on quit attempts were also insensitive to detect a change in trend (BF 0.71), and despite a greater step-level decline among those aged 16-17 (OR 0.311 versus OR 0.547, p = 0.025), quit attempts remained higher among those aged 16-17. Secondary analysis indicated that post-policy change, trends in current and ever smoking were linear for 16-17 year olds but quadratic for 18-24 year olds (slowing decline). CONCLUSION: There is some evidence from an assessment of long-term trends in the Smoking Toolkit Study that the increase in legal age-of-sale of cigarettes in England was associated with a greater long-term decline in ever smoking among those aged 16-17 compared with those aged 18-24.

Impact of the "Stoptober" Smoking Cessation Campaign in England From 2012 to 2017: A Quasiexperimental Repeat Cross-Sectional Study.

INTRODUCTION: Since 2012, England has an annual "Stoptober" campaign for collective smoking cessation. Our aim was to assess (1) overall impact of the Stoptober campaign on quit attempts over its first 6 years, (2) consistency of impact over the campaign years, and (3) the role of the campaign budget. METHODS: We used data of 51 399 adult smokers and ex-smokers in 132 repeat cross-sectional monthly surveys in England, 2007-2017. In a quasiexperimental design, adjusted logistic regression analyses compared past-month quit attempt rate between (1) October and other months in the year, between 2007-2011 and 2012-2017; (2) October and other months, across years 2012-2017; and (3) October and other months, between high-budget (2012-2015) and low-budget Stoptober campaigns (2016-2017). Bayes factors (BF) differentiated insensitive data and absence of an effect. RESULTS: (1) In 2012-2017, quit attempts were more prevalent in October versus other months (odds ratio [OR]: 1.24, 95% confidence interval [CI]: 1.00 to 1.53), whereas similar in 2007-2011 (OR: 0.95, 95% CI: 0.76 to 1.18; BF = 0.2); data were somewhat insensitive but supported this difference (OR: 1.30, 95% CI: 0.97 to 1.75; BF = 2.1). (2) In 2012-2017, quit attempt prevalence ranged from 3.1% to 8.5% in October and 5.0% to 7.3% in other months. The difference between October and other months was large in 2012 (absolute unadjusted difference of 3.3%; OR: 1.92, 95% CI: 1.23 to 2.98) and 2015 (3.1%; OR: 1.84, 95% CI: 1.14 to 2.95), but small in 2013-2014 and 2016-2017 (0.36 < BF < 1.02). (3) Data were somewhat insensitive but supported interaction with campaign budget (OR: 1.50, 95% CI: 0.92 to 2.44; BF = 2.2). DISCUSSION: In 2012-2017, there appears to have been an increase in past-month quit attempts during October in England. The increase was inconsistent across Stoptober campaigns and appears to have been greater when the campaign budget was higher. IMPLICATIONS: Over the first 6 years of Stoptober campaigns, there appears to have been an overall increase in past-month quit attempts during October in England, and the data imply that a sufficiently high budget contributes to greater impact of the Stoptober campaign. These findings encourage the further spread of the Stoptober campaign to other countries. Future research should clarify how increased quit attempts as a consequence of Stoptober translate into quit success and which of Stoptober's ingredients were most important in increasing quit attempts, especially among vulnerable groups.

Trends in Attempts to Quit Smoking in England Since 2007: A Time Series Analysis of a Range of Population-Level Influences.

AIM: To quantify population-level associations between quit attempts and factors that have varied across 2007-2017 in England. METHODS: Data from 51 867 past-year smokers participating in the Smoking Toolkit Study (a monthly cross-sectional survey of individuals aged 16+) were aggregated over an 11-year period. Time series analysis was undertaken using ARIMAX modeling. The input series were: (1) prevalence of smoking reduction using (a) e-cigarettes and (b) nicotine replacement therapy; (2) prevalence of roll-your-own tobacco use; (3) prevalence of (a) smoking and (b) non-daily smoking; (4) mass media expenditure; (5) average expenditure on smoking; (6) characteristics in the form of (a) prevalence of high motivation to quit, (b) average age, (c) proportion from lower social grades, and (d) average number of cigarettes smoked; and (7) implementation of tobacco control policies. RESULTS: There was a decline in the prevalence of quit attempts from 44.6% to 33.8% over the study period. The partial point-of-sale ban was associated with a temporary increase in quit attempt prevalence (Badjusted = 0.224%; 95% confidence interval [CI] 0.061 to 0.388). Quit attempts were positively associated with the prevalence of high motivation to quit (Badjusted = 0.165%;95% CI 0.048 to 0.282) and negatively associated with the mean age of smokers (Badjusted = -1.351%; 95% CI -2.168 to -0.534). All other associations were nonsignificant. CONCLUSION: Increases in the prevalence of high motivation to quit was associated with higher prevalence of attempts to quit smoking, while an increase in the mean age of smokers was associated with lower prevalence. The introduction of the partial point-of-sale ban appeared to have a temporary positive impact. IMPLICATIONS: This study provides insight into how monthly changes in a wide range of population-level factors are associated with changes in quit attempts over an extended time period in a country with a strong tobacco control climate. The findings suggest a need for intervention or policy to stimulate quit attempts in older smokers. Otherwise, increases in the mean age of a smokers appears likely to undermine wider efforts to promote quit attempts in a population.

'S'-shaped curve: modelling trends in smoking prevalence, uptake and cessation in Great Britain from 1973 to 2016.

BACKGROUND: It is believed that declines in smoking prevalence naturally slow over time as the smoking population 'hardens' and that progress has come primarily from reducing uptake rather than increasing cessation. To address these issues, we undertook the first formal attempt to model the trajectory of smoking prevalence and indices of uptake and cessation in Great Britain from 1973 to 2016. METHODS: Using data from the General Lifestyle Survey between 1973 and 2008, the Integrated Household Survey between 2009 and 2014 and the Annual Population Survey between 2015 and 2016, this study modelled year-on-year changes in smoking prevalence, ever-smoking in 18-24-year-olds as an index of uptake, and quit ratios as an index of cessation. RESULTS: For all three outcomes, changes over time were best fitted by what may be broadly characterised as 'S'-shaped curves: segmented functions characterised by initial rapid progress, a slowing or reversal, then renewed progress. Smoking prevalence in Great Britain showed a decelerating decline over time between 1973 and 2000, but then, after the introduction of the National 'Smoking Kills' tobacco control plan, the decline accelerated again and has remained nearly linear at -0.67 percentage points per year. Ever-smoking showed a decelerating decline which eventually ceased and began increasing around 1994 but then declined again after 2000. Quit ratios rose rapidly then slowed and then accelerated around 2000 and again more recently in 2013. CONCLUSION: Long-term trends in smoking prevalence, uptake and cessation have followed a broadly 'S'-shaped trend suggesting that they are responsive to major tobacco control initiatives. The decline in prevalence has resulted both from reductions in uptake and increases in cessation.

Are smokers who are regularly exposed to e-cigarette use by others more or less motivated to stop or to make a quit attempt? A cross-sectional and longitudinal survey.

BACKGROUND: Concerns have been raised that observing other people using e-cigarettes may undermine motivation to quit by renormalising smoking. This study aimed to explore associations between regular exposure to other people's e-cigarette use and motivation to stop smoking and quit attempts in smokers. METHODS: Data were from 12,787 smokers in England who participated in the Smoking Toolkit Study between November 2014 and May 2018. At baseline, respondents were asked whether anyone other than themselves regularly used an e-cigarette in their presence, whether they had made a quit attempt in the past year and how motivated they were to stop. Data at 6-month follow-up were available for 1580 respondents, who reported on whether they had attempted to quit in the past 6 months. RESULTS: Smokers who reported regular exposure to e-cigarette use by others were more likely than those who did not to have tried to stop smoking in the past year (32.3% vs. 26.8%; unadjusted RR 1.21, 95% CI 1.11-1.31) and have high motivation to quit (16.6% vs. 14.2%; unadjusted RR 1.17, 95% CI 1.05-1.30) but were not significantly more or less likely to make a quit attempt over the subsequent 6 months (34.4% vs. 31.3%; unadjusted RR 1.10, 95% CI 0.88-1.38). In models that adjusted for participants' own current e-cigarette use and unadjusted and adjusted models excluding current e-cigarette users from the sample, there were no significant associations between exposure to e-cigarette use by others and past quit attempts (RR 0.97-1.00), high current motivation to quit (RR 0.97-1.00) or prospective quit attempts (RR 0.94-1.12). In contrast, exposure to use of cigarettes was associated with low motivation to quit even after adjustment (RR 0.89) but not with quit attempts. Participants' own use of e-cigarette was strongly associated with high motivation to quit (RR 1.95) and past quit attempts (RR 2.14) and appeared to account for the bivariate associations with reported exposure to e-cigarettes. CONCLUSION: Smokers who report regular exposure to other people using e-cigarettes are more likely to report past quit attempts and high current motivation to quit, but there does not appear to be an independent association with motivation or quit attempts after adjustment for their own current use of e-cigarettes. In contrast, reported exposure to other people using cigarettes was independently and negatively associated with high motivation.