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Background Very few countries regulate maximum cigarette pack size. Larger, non-standard sizes are increasingly being introduced by the tobacco industry. Larger portion sizes increase food consumption; larger cigarette packs may similarly increase tobacco consumption. Here we consider the evidence for legislation to cap cigarette pack size to reduce tobacco-related harm. Aims and analysis We first describe the regulations regarding minimum and maximum pack sizes in the 12 countries that have adopted plain packaging legislation and describe the range of sizes available. We then discuss evidence for two key assumptions that would support capping pack size. First, regarding the causal nature of the relationship between pack size and tobacco consumption, observational evidence suggests that people smoke fewer cigarettes when using smaller packs. Secondly, regarding the causal nature of the relationship between reducing consumption and successful cessation, reductions in number of cigarettes smoked per day are associated with increased cessation attempts and subsequent abstinence. However, more experimental evidence is needed to infer the causal nature of these associations among general populations of smokers. Conclusion Cigarette pack size is positively associated with consumption and consumption is negatively associated with cessation. Based on limited evidence of the causal nature of these associations, we hypothesize that government regulations to cap cigarette pack sizes would positively contribute to reducing smoking prevalence.

The adoption and incentivisation of open and transparent research practices is critical in addressing issues around research reproducibility and research integrity. These practices will require training and funding. Individuals need to be incentivised to adopt open and transparent research practices (e.g., added as desirable criteria in hiring, probation, and promotion decisions, recognition that funded research should be conducted openly and transparently, the importance of publishers mandating the publication of research workflows and appropriately curated data associated with each research output). Similarly, institutions need to be incentivised to encourage the adoption of open and transparent practices by researchers. Research quality should be prioritised over research quantity. As research transparency will look different for different disciplines, there can be no one-size-fits-all approach. An outward looking and joined up UK research strategy is needed that places openness and transparency at the heart of research activity. This should involve key stakeholders (institutions, research organisations, funders, publishers, and Government) and crucially should be focused on action. Failure to do this will have negative consequences not just for UK research, but also for our ability to innovate and subsequently commercialise UK-led discovery.

Static high contrast ('dazzle') patterns, such as zigzags, have been shown to reduce the perceived speed of an object. It has not escaped our notice that this effect has possible military applications and here we report a series of experiments on humans, designed to establish whether dynamic dazzle patterns can cause distortions of perceived speed sufficient to provide effective defence in the field, and the extent to which these effects are robust to a battery of manipulations. Dynamic stripe patterns moving in the same direction as the target are found to increase the perceived speed of that target, whilst dynamic stripes moving in the opposite direction to the target reduce the perceived speed. We establish the optimum position for such dazzle patches; confirm that reduced contrast and the addition of colour do not affect the performance of the dynamic dazzle, and finally, using the CO 2 challenge, show that the effect is robust to stressful conditions.

The 2014 Research Excellence Framework (REF2014) was conducted to assess the quality of research carried out at higher education institutions in the UK over a 6 year period. However, the process was criticized for being expensive and bureaucratic, and it was argued that similar information could be obtained more simply from various existing metrics. We were interested in whether a prediction market on the outcome of REF2014 for 33 chemistry departments in the UK would provide information similar to that obtained during the REF2014 process. Prediction markets have become increasingly popular as a means of capturing what is colloquially known as the 'wisdom of crowds', and enable individuals to trade 'bets' on whether a specific outcome will occur or not. These have been shown to be successful at predicting various outcomes in a number of domains (e.g. sport, entertainment and politics), but have rarely been tested against outcomes based on expert judgements such as those that formed the basis of REF2014.

The 2014 Research Excellence Framework (REF2014) was conducted to assess the quality of research carried out at higher education institutions in the UK over a 6 year period. However, the process was criticized for being expensive and bureaucratic, and it was argued that similar information could be obtained more simply from various existing metrics. We were interested in whether a prediction market on the outcome of REF2014 for 33 chemistry departments in the UK would provide information similar to that obtained during the REF2014 process. Prediction markets have become increasingly popular as a means of capturing what is colloquially known as the 'wisdom of crowds', and enable individuals to trade 'bets' on whether a specific outcome will occur or not. These have been shown to be successful at predicting various outcomes in a number of domains (e.g. sport, entertainment and politics), but have rarely been tested against outcomes based on expert judgements such as those that formed the basis of REF2014.

Background Young people's experimentation with e-cigarettes has increased in recent years, although regular use remains limited. EU Tobacco Products Directive (TPD) regulations introduced packet warnings, advertising restrictions, and regulated nicotine strength from 2016, in part due to concerns regarding use by young people. This paper examines e-cigarette use trajectories before and after TPD. Methods E-cigarette use data were obtained from School Health Research Network/Health Behaviour in School-aged Children surveys in Wales and Smoking Drinking and Drug Use surveys in England. Data from Wales were analysed using segmented logistic regression, with before and after regression analyses of English data. Semi-structured group interviews included young people aged 14-16 years in Wales, England and Scotland in 2017 and 2018. Results In Wales, ever use of e-cigarettes increased over time, but under a range of assumptions, growth did not appear to continue post-TPD. A small and non-significant change in trend was observed post-implementation (OR=0.96; 95%CI=0.91 to 1.01), which increased in size and significance after adjusting for ever smoking (OR=0.93; 95%CI=0.88 to 0.98). There was little increase in regular e-cigarette use from 2015 to 2017 in Wales. However, ever and regular use increased from 2014 to 2016 in England. Young people in all nations described limited interactions with components of TPD, while describing e-cigarette use as a 'fad', which had begun to run its course. Conclusions This study provides preliminary evidence that young people's e-cigarette experimentation may be plateauing in UK nations. The extent to which this arises from regulatory changes, or due to a fad having begun to lose its appeal among young people in the UK countries, remains unclear. These trends contrast to those observed in North America, where newer products whose EU market entry and marketing been impacted by TPD, have gained traction among young people. Long-term monitoring of e-cigarette use trends and perceptions among young people remain vital.

Objectives: To estimate the causal effect of health conditions and risk factors on social and socioeconomic outcomes in UK Biobank. Evidence on socioeconomic impacts is important to understand because it can help governments, policy-makers and decision-makers allocate resources efficiently and effectively. Design: We used Mendelian randomization to estimate the causal effects of eight health conditions (asthma, breast cancer, coronary heart disease, depression, eczema, migraine, osteoarthritis, type 2 diabetes) and five health risk factors (alcohol intake, body mass index [BMI], cholesterol, systolic blood pressure, smoking) on 19 social and socioeconomic outcomes. Setting: UK Biobank. Participants: 337,009 men and women of white British ancestry, aged between 39 and 72 years. Main outcome measures: Annual household income, employment, deprivation (measured by the Townsend deprivation index [TDI]), degree level education, happiness, loneliness, and 13 other social and socioeconomic outcomes. Results: Results suggested that BMI, smoking and alcohol intake affect many socioeconomic outcomes. For example, smoking was estimated to reduce household income (mean difference = -24,394, 95% confidence interval (CI): -33,403 to -15,384), the chance of owning accommodation (absolute percentage change [APC] = -21.5%, 95% CI: -29.3% to -13.6%), being satisfied with health (APC = -32.4%, 95% CI: -48.9% to -15.8%), and of obtaining a university degree (APC = -73.8%, 95% CI: -90.7% to -56.9%), while also increasing deprivation (mean difference in TDI = 1.89, 95% CI: 1.13 to 2.64, approximately 236% of a decile of TDI). There was evidence that asthma increased deprivation and decreased both household income and the chance of obtaining a university degree, and migraine reduced the chance of having a weekly leisure or social activity, especially in men. For other associations, estimates were null. Conclusions: Higher BMI, alcohol intake and smoking were all estimated to adversely affect multiple social and socioeconomic outcomes. Effects were not detected between health conditions and socioeconomic outcomes using Mendelian randomization, with the exceptions of depression, asthma and migraines. This may reflect true null associations, selection bias given the relative health and age of participants in UK Biobank, and/or lack of power to detect effects.