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This study will examine how the prevalence of smoking, vaping and inhaled nicotine use has changed since the growth in disposable e-cigarette use in Great Britain. Full details are available in the pre-registered protocol and analysis plan.

Using data from the Health Survey for England 2019, we aim to estimate, among people who exclusively smoke, exclusively vape, and dual use, the percentage that report having recently used their nicotine products inside (anywhere inside, inside the home, inside the car, and/or inside other places). In exclusive users, we will also examine how the prevalence of inside vaping and smoking varies by age, sex, housing tenure, social grade, presence of a child or adult in the home, smoking and vaping history, and nicotine dependence.

A cross-sectional study using data from three waves of a repeated cross-sectional survey in GB: the Alcohol Toolkit Study (ATS).

Reducing smoking prevalence remains a priority for governments worldwide. Under the World Health Organisation’s Framework Convention on Tobacco Control, countries are required to promote “public awareness about the health risks of tobacco consumption and exposure to tobacco smoke, and about the benefits of the cessation of tobacco use and tobacco-free lifestyles” (1). To this end, tobacco control mass media campaigns are designed to increase the salience of the negative health consequences of smoking, promote quitting, and increasingly enable the use of evidence-based support (2). In this study, we consider the impact of mass media campaign expenditure in England on quit attempts among smokers with differing levels of motivation to stop smoking. While there is substantial evidence linking mass media campaigns with increased rates of success among smokers trying to quit (3–6), their impact on generating quit attempts is less clear. Several studies have reported increases in quit attempts related to mass media campaigns. For example, in the USA, a 3-month national anti-smoking campaign was associated with a 12% increase in quit attempts (7). Similarly, when England’s national ‘Stoptober’ campaign (which encourages smokers to quit for the month of October) was launched in 2012, a significant increase in quit attempts was seen in October vs. other months compared with previous years (8), which was inconsistently sustained in subsequent years as a function of campaign expenditure (9). However, a controlled study evaluating a mass media campaign targeting Vietnamese male smokers in California found no significant difference in quit attempts in the target group between the campaign region and a control region (Houston, Texas), where quit attempts also increased in the absence of a mass media campaign (10). Time series analyses using data representative of the adult population in England have found no significant association between tobacco control mass media campaign expenditure and the rate of quit attempts among smokers (4,11). Motivation to stop smoking is a key driver of quit attempts (12–14). If tobacco control mass media campaigns increase quit attempts to differing extents according to smokers’ level of motivation to stop, this could potentially account for the mixed evidence linking mass media campaigns and quit attempts. On the one hand, mass media campaigns may have comparatively more success in generating quit attempts among smokers with low or no motivation to stop. Increasing smokers’ motivation is one of the pathways through which tobacco control mass media campaigns may work to increase quitting (3,15). These campaigns can offer smokers new insights, increase the salience of smoking harms and the benefits of quitting, encourage them to reflect on the implications of their own behaviour, and raise awareness of the support available to them should they decide to try and quit. On the other hand, tobacco control mass media campaigns may be better comparatively at ‘converting’ quit attempts among highly motivated smokers, by directing them to support or simply prompting them to act on their intention to quit. Alternatively, there may be no moderating impact, with tobacco control mass media campaign expenditure affecting quit attempts similarly among smokers at every level of motivation. Understanding how far, and among whom, mass media campaigns increase quit attempts is important for designing and targeting campaigns to achieve the greatest impact and informing policy decisions around their continued investment. In England, large-scale tobacco control mass media campaigns have been run as part of a comprehensive national tobacco control programme since 1999. These campaigns have used a range of mass media, including television, radio and press, and often advertised free local stop smoking services, the national quitline, free quit support packs, and the national smoking cessation website. Expenditure on these campaigns has varied over time, providing a natural experiment within which to explore the impact of changes in mass media campaign intensity on quitting outcomes in the English population. This study aims to explore whether expenditure on tobacco control mass media campaigns moderates the association between motivation to stop smoking and future quit attempts. Specifically, we aim to address the following research question: 1. Among smokers in England, is the association between motivation to stop smoking at baseline and quit attempts 12 months later moderated by mean monthly tobacco control mass media expenditure between baseline and 12-month follow-up, after adjusting for age, sex, social grade, and region?

The New Zealand (NZ) government quickly identified the role e-cigarettes (ECs) have as a reduced-harm product to help smokers switch away from tobacco. ECs are included as an option in NZs smoking cessation treatment guidelines, and clear messaging regarding their use is promoted by NZ health authorities (https://vapingfacts.health.nz). The first ECs delivered nicotine at similar levels to nicotine replacement therapy (NRT) products, but with rapid product innovation, new pod-based devices using nicotine salts deliver nicotine at levels comparable to that of smoked tobacco. Given the long-term health effects of EC use remain uncertain and that not vaping (use of an EC) or smoking is ideal from a public health perspective, it’s important to offer support to people who wish to switch from ECs to no product use. However, little evidence exists on the best way to support people to move away from ECs, and no data exists for NZ. We have therefore designed a survey to collect information from vapers and ex-vapers about enablers and barriers to becoming vape-free.

BACKGROUND: Many veterans are eligible to receive prescriptions from community-based pharmacies. Swift and accurate review of prior authorization drug requests by the US Department of Veterans Affairs (VA) pharmacy is necessary to mitigate treatment delays, medication misuse, adverse drug events, medication errors, and unnecessary cost to the health care system. METHODS: We performed a retrospective review of community care prior authorization drug requests to assess the direct cost savings achieved through a centralized process and to characterize submitted requests. RESULTS: The centralized community care pharmacy team demonstrated a cost savings of $515,872.31 over 6 months and increased patient safety. Community care prior authorization drug requests had a 46.2% approval rate. Coordination of care took an average of 8 days. CONCLUSIONS: Use of a centralized community care pharmacy team could result in significant annual cost savings for the VA. Considering the approval rate seen in this study, VA could allocate resources to educate community-based prescribers about its formulary to increase the approval rate and reduce administrative burden for VA pharmacies and prescribers.

Paper aiming to characterise alcohol reduction attempts and their success among increasing and higher risk drinkers using a population survey in England.

Smokers who have previously made an unsuccessful quit attempt may infer that they must be addicted to smoking (i.e. ‘self-attribution of addiction’). Perceived addiction (PA) may in turn deter smokers from making a future quit attempt. To examine the impact of PA on the component parts of the quitting process, the present study aimed to assess the associations of PA with 1) past quit attempts; 2) current motivation to stop; 3) future quit attempts; and 4) quitting success.

Pre-registration (before conducting the data analysis) of the protocol for data analysis (v1.2 25/08/2020) for a planned manuscript entitled: Characterising the patterns of and factors associated with increased alcohol consumption since COVID-19 in the UK.

Pre-registered study protocol and analysis plan

Overall smoking prevalence is at an all-time low in England, at 15%(1) but there remains substantial differences between subgroups of the population. Smoking is a major driver of health inequalities, significantly contributing to the differences in rates of morbidity and premature mortality between the most and least deprived (2–4). In England, smoking rates are almost twice as high in those in routine and manual employment (18.9%) compared with those in professional and managerial trades (10.9%; (1)) and the gap has stubbornly persisted over a decade of declines in overall prevalence (5). In addition to occupational social grade, other indicators of socioeconomic position (SEP), have been associated with smoking status and prevalence, such as income, wealth, education, home and car ownership (2,6). Recent analyses have shown housing status, and in particular living in social housing (7) (i.e., local government assisted housing), to be the strongest independent predictor of smoking status (6). Like other health conditions, it is unlikely that not owning a home per se leads to smoking, but rather it is a proxy measure for the economic, social, environmental, and commercial factors - the ‘wider determinants’ of health which also lead people to smoke (8,9). These are broad influences, liable to change, and so monitoring over time which measures of SEP best predict leading causes preventable illness and death (10) is essential. Understanding which factors are independently associated, that is, most strongly predict against competing measures, is helpful for targeting finite resources to support smoking cessation treatment and to help researchers know which measures are best for assessing tobacco-related inequalities. However, as has been argued by others, trying to find the best single SEP predictor of health outcomes is theoretically limiting as this is only useful for interpreting the behaviour within a particular time and context (10). For example, car ownership was once a good indicator of material wealth by income and in turn associated with health behaviours and outcomes (11). The rise in car ownership, partly through greater affordability (12), means ownership alone no longer clearly distinguishes between social grade or income (although it may be useful as part of a composite score). Using several different measures of SEP can usefully capture changing trends and is important for evaluating the full contribution of SEP factors that lead to poorer health outcomes. However, this can be difficult in traditional regression models, given the high collinearity between measures of SEP (13), requiring researchers to select measures at the exclusion of others. This may lead to false conclusions or missing other important factors related to the health condition being studied. In a previous study (6), we examined six different measures of SEP (home ownership, income, occupational social grade, working status, car ownership and education) and smoking status between 2013-2019 using ridge regression analyses, a machine learning technique that allows an assessment of the contribution of each independent variable while taking account of high collinearity (10,13). Results showed home ownership was the strongest predictor, with those who reported not owning their own home having twice the odds of smoking compared with those who did. Occupational social grade, education and income were also good predictors, with a reduced odds among those in more advantaged positions being smokers. However, associations between car ownership and working status were weaker. A composite score of the six measures of SEP showed a gradient effect, with greater levels of disadvantage resulting in higher odds of smoking. In this present study, we will again examine the independent associations between these six measures of SEP and smoking status in England. We will extend our previous study by examining whether there were trends in these associations over a ten-year period (between 2010-2019). We propose several hypotheses: we expect the association between housing and smoking status to have strengthened. In England housing has become less affordable for a greater number of people, and there is a housing shortage and a lack of adequate housing, making housing status a contemporary marker for large health inequalities (8,14). Education and income are related to each other, and we would expect an association for both with smoking status, and the strength of the association with income to have increased owing to the rising cost of living, rising levels of income-related poverty and austerity (14). Given the social patterning of smoking related health inequalities that persist, we expect occupation social grade to remain strongly associated with smoking. We also expect the association between car ownership and smoking status to have not changed since our earlier study (6). Similarly, there was a weak association between working status and smoking status in our previous work, and we expect this relationship to remain weak, reflecting a more diverse nature of employment status and opportunities across the social grade spectrum (e.g., gig economy). Lastly, we expect composite scores of measures of SEP that reflect greater disadvantage to be most strongly predictive of smoking status and to demonstrate a gradient effect. These hypothesised relationships are presented in a directed acyclic graph (DAG; Figure 1), highlighting the potentially causal role of these factors on smoking, though not firmly determined they provide an overview of the complexity of the factors contributing to the health behaviour. It is hoped the results of this study can inform researchers and policy makers on the changing trends in SEP and smoking status. Using monthly cross-sectional data from the Smoking Toolkit Study in England between 2010 and 2019, this study will examine: 1. Do time trends across a ten-year period moderate the univariate associations between six different measures of SEP (occupation social grade, housing tenure, income, car ownership, education and employment status) and a composite of these with smoking status? 2. Has there been a change in the strongest independent socioeconomic position predictor of smoking over time?

Plan for the analysis of the primary outcome data from BupaQuit trial.

This study aims to compare mental health, assessed by past-month psychological distress, in adults by smoking status and continued use of nicotine-containing products among all adults who have stopped smoking.

Pre-registration (before conducting the data analysis) of the protocol for data analysis for a planned manuscript entitled: Perceived risk factors for Covid-19 and association with socio-demographic and health-related characteristics: Findings from the HEBECO study. The document provides the background for the project, research questions, outcomes and predictors of interest, and the planned analyses.

BupaQuit Appendix Supplementary Materials

Early in the COVID-19 pandemic, there was a strong a priori assumption that smoking was an increased risk factor for disease severity owing to a high incidence of respiratory illness and pre-existing poor lung function in long-term smokers (1,2). However, results of a living systematic meta-analysis indicate that current smokers appear to be at reduced risk of COVID-19 infection relative to non-smokers, and there is inconclusive evidence that smokers who are infected are at increased risk of more severe disease (3). Former smokers appear to be at increased risk of severe disease and mortality from COVID-19 (3). Nonetheless, the pandemic has triggered some smokers to consider improving their health; we found that rates of smoking cessation were higher post March 2020 lockdown compared with the pre-pandemic period in England (4). Evidence from the UK also shows that concern around infection is more common in current compared with never smokers, and smokers report being less able to enact personal protective behaviours (5). In the UK, several health agencies recognised this as a ‘tobacco teachable moment’, and there was a well a publicised ‘Quit for Covid’ public health campaign (6). Even within the context of the pandemic, smoking continues to be a public health priority. This is salient in England, where there is less than a decade to achieve the public health goal of being smoke free by 2030 (defined as a smoking prevalence of 10 cigarettes per day). Since the data presented above (5,9) were collected, there have been several important developments. Firstly, England has experienced two more lockdowns. Second, the absolute death and population infection rate has risen. These longer-term effects of the pandemic may have continued to impact people’s decisions to change their behaviour, especially modifying behaviours like smoking. Third, there has been an increase in access to and uptake of COVID-19 testing in England, and thus, the accuracy of whether people have had COVID-19 in self-reports is now likely to be greater than early in the history of the pandemic. Assessing the association of COVID-19 infection status on current and future changes to smoking with greater accuracy is now possible.

This study aims to investigate differences in the prevalence and characteristics of smoking and vaping among people with and without serious past month psychological distress, and with and without an ever MHC diagnosis in England; using pooled monthly cross-sectional data from the Smoking Toolkit Study collected between April 2020 and February 2021

Pre-registration (before conducting the data analysis) of the protocol for data analysis for a planned manuscript entitled: Associations between e-cigarette use and Covid-19. Cross-sectional findings from the HEBECO study.

Pre-registration (before conducting the data analysis) of the protocol for data analysis for a planned manuscript entitled: "Food consumption behaviour among UK adults during the Covid-19 pandemic: findings from the HEBECO study". The document provides the background for the project, research questions, outcomes and predictors of interest, and the planned analyses.

This study uses a population survey to investigate beliefs and attitudes concerning when it is appropriate to address alcohol consumption in health care and to identify the characteristics of those who are most and least supportive, respectively, of addressing alcohol consumption in health care.

Using a nationally representative repeat cross-sectional survey in England, this study aimed to i) assess inequalities in smoking and quitting behaviour among all adults in recent years by children in the household and ii) explore trends in smoking prevalence between 2013 and 2019 among adults with and without children in the household by SEP.

Pre-registration (before conducting the data analysis) of the protocol for data analysis (v3. 06.09.2020) for a planned manuscript entitled: Associations between e-cigarette use and Covid-19: cross-sectional findings from the HEBECO study.

Smoking prevalence is declining in England but remains persistently high in ‘lower’ socioeconomic position (SEP) groups compared with more advantaged groups. Smoking is a significant contributor to health and social inequalities. There is therefore an urgent need to identify factors that underpin this disparity and use this information to inform public health policies and smoking cessation interventions tailored to the needs of lower SEP groups. Lower SEP is associated with multiple stressors, including financial insecurity, ill health and reduced social capital, with lower SEP smokers experiencing greater nicotine dependence and therefore greater negative affect from withdrawal. Although research indicates that smoking to relieve stress is a commonly endorsed motive overall, it is unclear whether this is more pronounced in lower (compared with higher) SEP groups and if so, whether this has a negative impact on motivation to stop and future quit attempts. We therefore aim to examine whether smoking to relieve stress is associated with motivation to stop and attempts to quit in the next 12 months, and whether these associations are moderated by SEP. This will be an observational study, with cross-sectional and prospective survey data from the nationally representative Smoking Toolkit Study in England.

The research aims, analysis plan and priors for this study were selected before data analysis. Details of these are available in the pre-analysis protocol and prior selection document.

Methods paper regarding the smoking and alcohol toolkit study across constituent nations in Great Britain.

In this paper, we use data from two sources, the Alcohol Toolkit13 (a representative population survey in England) and the Try Dry app which is freely available internationally (with users from 175 countries), although here we focus on data from England. We compare the proportion of and sociodemographic and drinking characteristics of individuals who report participating in Dry January and those who signed up to use the Try Dry app to do so, in 2021 relative to 2020.

In this exploratory study, we will use supervised machine learning algorithms (e.g. Random Forest, Support Vector Machine) to predict group- and individual-level lapse risk in smokers attempting to stop with a popular smartphone app.

The ongoing COVID-19 pandemic, caused by the SARS-CoV-2 virus, is placing an unprecedented burden on healthcare and social systems in the UK and globally: with community transmission established in February 2020, COVID-19 has led to >9 million infections in the UK, with >140,000 premature deaths as of November 2021. At the start of the pandemic, many researchers and healthcare professionals hypothesised that tobacco smoking would be a risk factor for SARS-CoV-2 infection and subsequent disease progression. This was based on prior knowledge of biological, social, and behavioural factors that increase the risk of viral transmission. First, SARS­CoV­2 enters epithelial cells through the angiotensin-converting enzyme 2 (ACE­2) receptor, with evidence suggesting that gene expression and subsequent receptor levels are elevated in smokers, thus potentially putting smokers at higher risk of contracting SARS­CoV­2 if exposed to the virus. However, reduced receptor levels in smokers have also been reported. Second, both current and former smoking increase the risk of other respiratory viral and bacterial infections. Third, behavioural factors involved in smoking (e.g. regular hand-to-mouth movements) may increase SARS­CoV­2 infection through increased exposure and onward transmission once infected. Fourth, individuals from lower (compared with higher) socioeconomic position (SEP) groups are more likely to be smokers and are also more likely to live in overcrowded housing and/or be employed in occupations with increased viral exposure (e.g., supermarkets, warehouses, public transport). If smoking confers an increased risk of infection, this would further emphasise the need for public health efforts to support smoking cessation, with campaigns potentially leveraging motivation to quit to protect oneself and others from COVID-19. We aim to examine, in the nationally representative UK Office for National Statistics (ONS) COVID-19 Infection Survey, whether 1) current vs. never smoking is associated with reduced risk of a) testing positive for SARS-CoV-2; and b) SARS-CoV-2 seropositivity; 2) former vs. never smoking is associated with increased risk of a) testing positive for SARS-CoV-2; and b) SARS-CoV-2 seropositivity; 3) among nicotine users, current cigarette smoking vs. no cigarette smoking is associated with equivocal risk of a) testing positive for SARS-CoV-2; and b) SARS-CoV-2 seropositivity; 4) among former and never smokers, e-cigarette use vs. no e-cigarette is associated with reduced risk of a) testing positive for SARS-CoV-2; and b) SARS-CoV-2 seropositivity; 5) for the above questions, whether associations vary over the course of the pandemic; and 6) in those testing positive for SARS-CoV-2, current smoking/nicotine use is associated with a) greater mean PCR cycle threshold values; and b) a lower number of self-reported symptoms.

This project details the process of refining the content and design of the Drink Less app to improve its usability and effectiveness using mixed methods.

In this study, we use data from the baseline (recorded between April and June 2020) and 6-month follow-up survey of the HEBECO study (recorded between November 2020 and January 2021) to longitudinally examine the longer-term effects of COVID-19 on drinking behavior in the UK. As we know that there is divergence in changes to drinking in lockdown between men and women and differences in the way men and women have been affected by changes in circumstances under lockdown all analyses will be stratified by gender.

Analysis plan

Study of the concurrent validity of a fast Estimator of Weekly Alcohol Consumption (EWAC) based on the Extended Alcohol Use Disorders Identification Test (Extended AUDIT) compared at individual and population level

Pre-registration of the protocol for data analysis (v6) for a planned manuscript entitled: "Associations between sociodemographic and health characteristics and changes in the use of and future interest in digital and non-digital health aids for health behaviour change during the Covid-19 pandemic in the UK."