To investigate the population attributable fraction due to elevated lipoprotein (a) (Lp(a)) and the utility of measuring Lp(a) in cardiovascular disease (CVD) risk prediction. In 413 734 participants from UK Biobank, associations of serum Lp(a) with composite fatal/non-fatal CVD (n = 10 066 events), fatal CVD (n = 3247), coronary heart disease (CHD; n = 18 292), peripheral vascular disease (PVD; n = 2716), and aortic stenosis (n = 901) were compared using Cox models. Median Lp(a) was 19.7 nmol/L (interquartile interval 7.6–75.3 nmol/L). About 20.8% had Lp(a) values >100 nmol/L; 9.2% had values >175 nmol/L. After adjustment for classical risk factors, 1 SD increment in log Lp(a) was associated with a hazard ratio for fatal/non-fatal CVD of 1.12 [95% confidence interval (CI) 1.10–1.15]. Similar associations were observed with fatal CVD, CHD, PVD, and aortic stenosis. Adding Lp(a) to a prediction model containing traditional CVD risk factors in a primary prevention group improved the C-index by +0.0017 (95% CI 0.0008–0.0026). In the whole cohort, Lp(a) above 100 nmol/L was associated with a population attributable fraction (PAF) of 5.8% (95% CI 4.9–6.7%), and for Lp(a) above 175 nmol/L the PAF was 3.0% (2.4–3.6%). Assuming causality and an achieved Lp(a) reduction of 80%, an ongoing trial to lower Lp(a) in patients with CVD and Lp(a) above 175 nmol/L may reduce CVD risk by 20.0% and CHD by 24.4%. Similar benefits were also modelled in the whole cohort, regardless of baseline CVD. Population screening for elevated Lp(a) may help to predict CVD and target Lp(a) lowering drugs, if such drugs prove efficacious, to those with markedly elevated levels.

Observational studies aiming to estimate causal effects often rely on conceptual frameworks that are unfamiliar to many researchers and practitioners. We provide a clear, structured overview of key concepts and terms, intended as a starting point for readers unfamiliar with the causal inference literature. First, we introduce theoretical frameworks underlying causal effect estimation methods: the counterfactual theory of causation, the potential outcomes framework, structural equations and directed acyclic graphs. Second, we define the most common causal effect estimands, and the issues of effect measure modification, interaction and mediation (direct and indirect effects). Third, we define the assumptions required to estimate causal effects: exchangeability, positivity, consistency and non-interference. Fourth, we define and explain biases that arise when attempting to estimate causal effects, including confounding, collider bias, selection bias and measurement bias. Finally, we describe common methods and study designs for causal effect estimation, including covariate adjustment, G-methods and natural experiment methods.

Drink driving is an important risk factor for road traffic accidents (RTAs), which cause high levels of morbidity and mortality globally. Lowering the permitted blood alcohol concentration (BAC) for drivers is a common public health intervention that is enacted in countries and jurisdictions across the world. In Scotland, on Dec 5, 2014, the BAC limit for drivers was reduced from 0·08 g/dL to 0·05 g/dL. We therefore aimed to evaluate the effects of this change on RTAs and alcohol consumption. In this natural experiment, we used an observational, comparative interrupted time-series design by use of data on RTAs and alcohol consumption in Scotland (the interventional group) and England and Wales (the control group). We obtained weekly counts of RTAs from police accident records and we estimated weekly off-trade (eg, in supermarkets and convenience stores) and 4-weekly on-trade (eg, in bars and restaurants) alcohol consumption from market research data. We also used data from automated traffic counters as denominators to calculate RTA rates. We estimated the effect of the intervention on RTAs by use of negative binomial panel regression and on alcohol consumption outcomes by use of seasonal autoregressive integrated moving average models. Our primary outcome was weekly rates of RTAs in Scotland, England, and Wales. This study is registered with ISRCTN, number ISRCTN38602189. We assessed the weekly rate of RTAs and alcohol consumption between Jan 1, 2013, and Dec 31, 2016, before and after the BAC limit came into effect on Dec 5, 2014. After the reduction in BAC limits for drivers in Scotland, we found no significant change in weekly RTA rates after adjustment for seasonality and underlying temporal trend (rate ratio 1·01, 95% CI 0·94-1·08; p=0.77) or after adjustment for seasonality, the underlying temporal trend, and the driver characteristics of age, sex, and socioeconomic deprivation (1·00, 0·96-1·06; p=0·73). Relative to RTAs in England and Wales, where the reduction in BAC limit for drivers did not occur, we found a 7% increase in weekly RTA rates in Scotland after this reduction in BAC limit for drivers (1·07, 1·02-1·13; p=0·007 in the fully-adjusted model). Similar findings were observed for serious or fatal RTAs and single-vehicle night-time RTAs. The change in legislation in Scotland was associated with no change in alcohol consumption, measured by per-capita off-trade sales (-0·3%, -1·7 to 1·1; p=0·71), but a 0·7% decrease in alcohol consumption measured by per-capita on-trade sales (-0·7%, -0·8 to -0·5; p<0·0001). Lowering the driving BAC limit to 0·05 g/dL from 0·08 g/dL in Scotland was not associated with a reduction in RTAs, but this change was associated with a small reduction in per-capita alcohol consumption from on-trade alcohol sales. One plausible explanation is that the legislative change was not suitably enforced-for example with random breath testing measures. Our findings suggest that changing the legal BAC limit for drivers in isolation does not improve RTA outcomes. These findings have significant policy implications internationally as several countries and jurisdictions consider a similar reduction in the BAC limit for drivers. National Institute for Health Research Public Health Research Programme.

Background: Frailty and multimorbidity are common in type 2 diabetes (T2D), including people <65 years. Guidelines recommend adjustment of treatment targets in people with frailty or multimorbidity. It is unclear how recommendations to adjust treatment targets in people with frailty or multimorbidity should be applied to different ages. We assess implications of frailty/multimorbidity in middle/older-aged people with T2D. Methods: We analysed UK Biobank participants (n = 20,566) with T2D aged 40–72 years comparing two frailty measures (Fried frailty phenotype and Rockwood frailty index) and two multimorbidity measures (Charlson Comorbidity index and count of long-term conditions (LTCs)). Outcomes were mortality, Major Adverse Cardiovascular Event (MACE), hospitalization with hypoglycaemia or fall/fracture. Results: Here we show that choice of measure influences the population identified: 42% of participants are frail or multimorbid by at least one measure; 2.2% by all four measures. Each measure is associated with mortality, MACE, hypoglycaemia, and fall or fracture. The absolute 5-year mortality risk is higher in older versus younger participants with a given level of frailty (e.g. 1.9%, and 9.9% in men aged 45 and 65, respectively, using frailty phenotype) or multimorbidity (e.g. 1.3%, and 7.8% in men with 4 LTCs aged 45 and 65, respectively). Using frailty phenotype, the relationship between higher HbA1c and mortality is stronger in frail compared with pre-frail or robust participants. Conclusions: Assessment of frailty/multimorbidity should be embedded within routine management of middle-aged and older people with T2D. Method of identification as well as features such as age impact baseline risk and should influence clinical decisions (e.g. glycaemic control).

Natural experiments are widely used to evaluate the impacts on health of changes in policies, infrastructure, and services. The UK Medical Research Council (MRC) and National Institute for Health and Care Research (NIHR) have published a new framework for conducting and using evidence from natural experimental evaluations. The framework defines key concepts and describes recent advances in designing and planning evaluations of natural experiments, including the relevance of a systems perspective, mixed methods, and stakeholder involvement. It provides an overview of the strengths, weaknesses, applicability, and limitations of the range of methods now available, and makes good practice recommendations for researchers, funders, publishers, and users of evidence.

Background and Aims: On 1 May 2018, Scotland became the first country in the world to introduce minimum unit pricing (MUP), a strength‐based floor price below which alcohol cannot be sold, across all alcoholic beverages. The legislation contains a sunset clause meaning a comprehensive mixed‐methods evaluation of its impact across a range of outcomes will inform whether it will continue beyond its sixth year. In this study, we assessed the impact of MUP on off‐trade alcohol sales (as a proxy for consumption) after its first year. Design, Setting and Participants: Controlled interrupted time–series regression was used to assess the impact of MUP on alcohol sales among off‐trade retailers in Scotland in the year after it was introduced, with England and Wales (EW) being the control group. In adjusted analyses, we included household disposable income, on‐trade alcohol sales and substitution between beverage categories as covariates. Measurements: Weekly data on the volume of pure alcohol sold by off‐trade retailers in Scotland and EW between January 2013 and May 2019, expressed as litres of pure alcohol per adult. Findings: The introduction of MUP in Scotland was associated with a 3.5% [95% confidence interval (CI) = 2.2–4.9%] reduction in off‐trade alcohol sales per adult after adjustment for the best available geographical control, disposable income and substitution. In unadjusted analysis, the introduction of MUP was associated with a 2.0% (95% CI = 0.4–3.6%) reduction in off‐trade alcohol sales per adult in Scotland. In EW, there was a 2.4% (95% CI = 0.8–4.0%) increase during the same time‐period. The reduction in off‐trade alcohol sales in Scotland was driven by reduced sales of spirits, cider and perry beverage categories. Conclusion: The implementation of minimum unit pricing for alcohol in Scotland in 2018 appears to have been associated with a reduction in off‐trade alcohol sales after its first year.