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Aims This paper estimates how far monthly changes in prevalence of cigarette smoking, motivation to quit and attempts to stop smoking have been associated with changes in prevalence of high-risk drinking, and motivation and attempts to reduce alcohol consumption in England. Design Data were used from the Alcohol and Smoking Toolkit Studies between April 2014 and June 2016. These involve monthly household face-to-face surveys of representative samples of ~1700 adults in England. Measurements Autoregressive Integrated Moving Average with Exogeneous Input (ARIMAX) modelling was used to assess the association over time between monthly prevalence of (a) smoking and high-risk drinking; (b) high motivation to quit smoking and high motivation to reduce alcohol consumption; and (c) attempts to quit smoking and attempts to reduce alcohol consumption. Findings Mean smoking prevalence over the study period was 18.6% and high-risk drinking prevalence was 13.0%. A decrease of 1% of the series mean smoking prevalence was associated with a reduction of 0.185% of the mean prevalence of high-risk drinking 2 months later [95% confidence interval (CI) = 0.033 to 0.337, P = 0.017]. A statistically significant association was not found between prevalence of high motivation to quit smoking and high motivation to reduce alcohol consumption (β = 0.324, 95% CI = -0.371 to 1.019, P = 0.360) or prevalence of attempts to quit smoking and attempts to reduce alcohol consumption (β = −0.026, 95% CI = -1.348 to 1.296, P = 0.969). Conclusion Between 2014 and 2016, monthly changes in prevalence of smoking in England were associated positively with prevalence of high-risk drinking. There was no significant association between motivation to stop and motivation to reduce alcohol consumption, or attempts to quit smoking and attempts to reduce alcohol consumption. [ABSTRACT FROM AUTHOR]

Background and aims: Harmful alcohol use places a significant burden on health services. Sophisticated nowcasting and forecasting methods could support service planning, but their use in public health has been limited. We aimed to use a novel analysis framework, combined with routine public health data, to improve now‐ and forecasting of alcohol‐related harms. Design We used Bayesian structural time–series models to forecast alcohol‐related hospital admissions for 2020/21 (from 2015 to 2016). Setting: England. Participants: We developed separate models for each English lower‐tier local authority. Measurements Our primary outcome was alcohol‐related hospital admissions. Model covariates were population size and age‐structure. Findings Nowcasting validation indicated adequate accuracy, with 5‐year nowcasts underestimating admissions by 2.2% nationally and 3.3% locally, on average. Forecasts indicated a 3.3% increase in national admissions in 2020/21, corresponding to a 0.2% reduction in the crude rate of new admissions, due to population size changes. Locally, the largest increases were forecast in urban, industrial and coastal areas and the largest decreases in university towns and ethnically diverse areas. Conclusions: In 2020/21, alcohol‐related hospital admissions are expected to increase in urban and coastal areas and decrease in areas associated with inward migration of younger people, including university towns and areas with greater ethnic diversity. Bayesian structural time–series models enable investigation of the future impacts of alcohol‐related harms in population subgroups and could improve service planning and the evaluation of natural experiments on the impact of interventions to reduce the societal impacts of alcohol. [ABSTRACT FROM AUTHOR]

Aims Most people who quit smoking for a short term will return to smoking again in 12 months. We tested whether self-help booklets can reduce relapse in short-term quitters after receiving behavioural and pharmacological cessation treatment. Design A parallel-arm, pragmatic individually randomized controlled trial. Setting Smoking cessation clinics in England. Participants People who stopped smoking for 4 weeks after receiving cessation treatment in stop smoking clinics. Intervention Participants in the experimental group (n = 703) were mailed eight booklets, each of which taught readers how to resist urges to smoke. Participants in the control group (n = 704) received a leaflet currently used in practice. Measurements The primary outcome was prolonged, carbon monoxide-verified abstinence from months 4 to 12. The secondary outcomes included 7-day self#x02010;reported abstinence at 3 and 12 months. Mixed-effects logistic regression was used to estimate treatment effects and to investigate possible effect modifying variables. Findings There were no statistically significant differences between the groups in prolonged abstinence from months 4 to 12 (36.9% versus 38.6%; odds ratio 0.93, 95% confidence interval 0.75-1.16; P = 0.524). In addition, there were no significant differences between the groups in any secondary outcomes. However, people who reported knowing risky situations for relapse and using strategies to handle urges to smoke were less likely to relapse. Conclusions In people who stop smoking successfully with behavioural support, a comprehensive self-help educational programme to teach people skills to identify and respond to high-risk situations for return to smoking did not reduce relapse. [ABSTRACT FROM AUTHOR]

Aims To estimate the effect on drug misuse treatment completion of a pilot scheme to pay service providers according to rates of recovery. Design A controlled, quasi-experimental (difference-in-differences) observational study using multi-level random effects logistic regression. Setting Drug misuse treatment providers in all 149 commissioning areas in England in the financial years 2011-12 and 2012-13. Participants Service users treated in England in 2011-12 and 2012-13. Intervention and comparators Linkage of provider payments to performance indicators in eight pilot commissioning areas in England compared with all 141 non-pilot commissioning areas in England. Measurements Recovery was measured by successful completion of treatment (free from drugs of dependence) and engagement with services was measured by rates of declining to continue with treatment. Findings Following the introduction of the pilot scheme, service users treated in pilot areas were 1.3 percentage points [odds ratio (OR) = 0.859; 95% confidence interval (CI) = 0.788, 0.937] less likely to complete treatment compared with those treated in comparison areas. Service users treated in pilot areas were 0.9 percentage points (OR = 2.934; 95% CI = 2.094, 4.113) more likely to decline to continue with treatment compared with those treated in comparison areas. Conclusions In the first year of the pilot 'Payment by Results for Drugs Recovery' scheme in England, linking payments to outcomes reduced the probability of completing drug misuse treatment and increased the proportion service users declining to continue with treatment. [ABSTRACT FROM AUTHOR]

Aim To examine whether government-funded tobacco control television advertising shown in England between 2002 and 2010 reduced adult smoking prevalence and cigarette consumption. Design Analysis of monthly cross-sectional surveys using generalised additive models. Setting England. Participants More than 80 000 adults aged 18 years or over living in England and interviewed in the Opinions and Lifestyle Survey. Measurements Current smoking status, smokers' daily cigarette consumption, tobacco control gross rating points ( GRPs-a measure of per capita advertising exposure combining reach and frequency), cigarette costliness, tobacco control activity, socio-demographic variables. Findings After adjusting for other tobacco control policies, cigarette costliness and individual characteristics, we found that a 400-point increase in tobacco control GRPs per month, equivalent to all adults in the population seeing four advertisements per month (although actual individual-level exposure varies according to TV exposure), was associated with 3% lower odds of smoking 2 months later [odds ratio ( OR) = 0.97, 95% confidence interval ( CI) = 0.95, 0.999] and accounted for 13.5% of the decline in smoking prevalence seen over this period. In smokers, a 400-point increase in GRPs was associated with a 1.80% (95% CI = 0.47, 3.11) reduction in average cigarette consumption in the following month and accounted for 11.2% of the total decline in consumption over the period 2002-09. Conclusion Government-funded tobacco control television advertising shown in England between 2002 and 2010 was associated with reductions in smoking prevalence and smokers' cigarette consumption. [ABSTRACT FROM AUTHOR]

To examine the association between weight change and baseline body mass index (BMI) over 8 years in a cohort of continuing and quitting smokers. Prospective cohort. Oxfordshire general practices nicotine patch/placebo trial with 8-year follow-up. Eighty-five participants were biochemically proven abstinent at 3, 6, 12 months and 8 years (abstainers). A total of 613 smoked throughout the 8 years (smokers), 26 quit for a whole year but were smoking again by 8 years (relapsed); 116 smoked for the first year but were abstinent at 8 years (late abstainers). Weight and BMI was measured at baseline and at 8 years. Regression models were used to examine weight gain by smoking status and the association of BMI at the time of quitting. Abstainers gained 8.79 kg [standard deviation (SD) 6.36; 95% confidence interval (CI) 7.42, 10.17]. Smokers gained 2.24 kg (SD 6.65; 95% CI 1.7, 2.77). Relapsed smokers gained 3.28 kg (SD 7.16; 95% CI 0.328, 6.24). Late abstainers gained 8.33 kg (SD 8.04; 95% CI 6.85, 9.81). The association between baseline BMI and weight change was modified by smoking status. In smokers there was a negative linear association of BMI, while in abstainers a J-shaped curve fitted best. These models estimated weight change over 8 years in abstainers of +9.8 kg, +7.8 kg, +10.2 kg, +19.4 kg and in smokers of +3.9 kg, +2.6 kg, 1.0 kg and −0.8 kg, where BMI was 18, 23, 29 and 36, respectively. Obese smokers gain most weight on quitting smoking, while obese continuing smokers are likely to remain stable or lose weight. Obese quitters have the greatest need for interventions to ameliorate weight gain. [ABSTRACT FROM AUTHOR]