The coronavirus disease 2019 (COVID-19) pandemic has placed significant burden on healthcare systems. We compared Clostridioides difficile infection (CDI) epidemiology before and during the pandemic across 71 hospitals participating in the Canadian Nosocomial Infection Surveillance Program. Using an interrupted time series analysis, we showed that CDI rates significantly increased during the COVID-19 pandemic.

Susceptibility to infection such as SARS-CoV-2 may be influenced by host genotype. TwinsUK volunteers (n = 3261) completing the C-19 COVID-19 symptom tracker app allowed classical twin studies of COVID-19 symptoms, including predicted COVID-19, a symptom-based algorithm to predict true infection, derived from app users tested for SARS-CoV-2. We found heritability of 49% (32−64%) for delirium; 34% (20−47%) for diarrhea; 31% (8−52%) for fatigue; 19% (0−38%) for anosmia; 46% (31−60%) for skipped meals and 31% (11−48%) for predicted COVID-19. Heritability estimates were not affected by cohabiting or by social deprivation. The results suggest the importance of host genetics in the risk of clinical manifestations of COVID-19 and provide grounds for planning genome-wide association studies to establish specific genes involved in viral infectivity and the host immune response.

The Scaling-up Health-Arts Programme: Implementation and Effectiveness Research (SHAPER) project is the world's largest hybrid study on the impact of the arts on mental health embedded into a national healthcare system. This programme, funded by the Wellcome Trust, aims to study the impact and the scalability of the arts as an intervention for mental health. The programme will be delivered by a team of clinicians, research scientists, charities, artists, patients and healthcare professionals in the UK's National Health Service (NHS) and the community, spanning academia, the NHS and the charity sector. SHAPER consists of three studies – Melodies for Mums, Dance for Parkinson's, and Stroke Odysseys – which will recruit over 800 participants, deliver the interventions and draw conclusions on their clinical impact, implementation effectiveness and cost-effectiveness. We hope that this work will inspire organisations and commissioners in the NHS and around the world to expand the remit of social prescribing to include evidence-based arts interventions.

Glycemic, insulinemic and lipemic postprandial responses are multi-factorial and contribute to diabetes, obesity and CVD. The aim of the PREDICT I study is to assess the genetic, metabolic, metagenomic, and meal-context contribution to postprandial responses, integrating the metabolic burden and gut microbiome to predict individual responses to food using a machine learning algorithm.

A multi-center postprandial study of 1,000 individuals from the UK (unrelated, identical and non-identical twins) and 100 unrelated individuals from the US, assessed postprandial (0–6h) metabolic responses to sequential mixed-nutrient dietary challenges (50 g fat and 85 g carbohydrate at 0 h; 22 g fat and 71 g carbohydrate at 4h) in a clinic setting. Glycemic responses to 5 duplicate isocaloric meals of different macronutrient content and self-selected meals (> 100,000), were tested at home using a continuous glucose monitor (CGM). Baseline factors included metabolomics, genomics, gut metagenomics and body composition. Genetic contributions to postprandial responses were determined by classical twin methods.

Inter-individual variability in postprandial responses (glucose, insulin and triacylglycerol (TG)) was high in the clinic setting: iAUC IQR (median) was (n = 644); glucose (0–2h) 1.97 (1.89) mmol/L.h, insulin (0–2h) 45.6 (67.7) mIU/L.h and TG (0–6h) 2.37 (2.42) mmol/L.h. The unadjusted genetic contribution for glucose, insulin and TG responses were 54%, 29% and 27% respectively. Within-individual concordance (ICC) in glucose responses (iAUC 0–2h) for at home duplicate isocaloric meals was moderate-to-high, depending on the test meal: ICC (95%CI) was; high carbohydrate 0.62 (0.58,0.66), (carbohydrate = 95g/76% energy; n = 764), average lunch 0.57 (0.53, 0.62) (carbohydrate = 68g/54% energy; n = 763), OGTT 0.65 (0.61,0.70) (carbohydrate = 75 g; n = 754), high fat 0.35 (0.28, 0.41) (fat = 40g/71% energy; n = 576) and high protein 0.56, (0.48,0.62) (protein = 41g/32% energy; n = 364). An interim machine learning algorithm predicted 46% of the variation in glycemic responses based on meal content, meal context and participant's baseline characteristics, excluding genetic and microbiome features. Only 29% of variation could be explained by the macronutrient content of the meal.

This is the most comprehensive postprandial study performed to date. The large and modifiable variation in metabolic responses to identical meals in healthy people explains why ‘one size fits all’ nutritional guidelines are problematic. The genetic component to these responses is moderate, leaving the majority of the variation potentially modifiable. By collecting information on glucose responses to > 100,000 meals, alongside environmental, genetic and microbiome variables, we will have excellent power to use machine learning to optimise and predict individual responses to foods.

Postprandial lipemia is an independent risk factor for CVD, due to effects on lipoprotein remodelling, oxidative stress, inflammation, haemostasis and endothelial dysfunction. However, it is unknown whether the total, peak or duration of the lipemic response determines risk. The PREDICT I study is the largest study to date to measure postprandial lipemic responses and intermediary acutely changing cardiometabolic risk factors at multiple time points using a standardized test meal model.

A multi-center postprandial study of 1,000 individuals from the UK (unrelated, identical and non-identical twins) and 100 unrelated individuals from the US, assessed postprandial (hourly 0–6h) metabolic responses to sequential mixed-nutrient dietary challenges (50 g fat, 85 g carbohydrate at 0 h; 22 g fat, 71 g carbohydrate at 4h) in a clinic setting. We investigated the relationship of different postprandial triacylglycerol (TG) measures (4 and 6 h TG iAUC, 4 and 6 h TG concentration, 4 and 6 h TG increase from fasting) with lipoprotein remodelling (XXL-VLDL (including chylomicron remnants and VLDL particles) and XL-VLDL particle concentrations (average diameters > 75, 64 nm respectively), HDL-C) and levels of glycosylated acute phase proteins (GlycA; marker of cardiovascular inflammation), all of which have been implicated as independent predictors of CVD risk.

Following adjustment (for use of medication, demographic characteristics, fasting TG, insulin and glucose levels), all six postprandial TG measures (4 and 6 h TG iAUC, 4 and 6 h TG concentration, 4 and 6 h TG increase from fasting) were strongly correlated with markers of atherogenic lipoprotein remodelling and the marker of cardiovascular inflammation (GlycA). The strongest correlation (interim analysis) was observed for the 6 h TG increase from fasting (all P < 0.001, Pearson's coefficient r = 0.94 [95%CI's; 0.93, 0.95] for XXL-VLDL-P; r = 0.95 [95%CI's; 0.95, 0.96] for XL-VLDL-P; r = 0.89 [95%CI's; 0.88, 0.91] for GlycA ; r = -0.61 [95%CI's; -0.66, -0.55] for HDL-C). Inter-individual variability in postprandial lipemic responses was high in the tightly controlled clinic setting (interim analysis, n = 656); IQR (median) was; iAUC (0–6h) 2.39 (2.31) mmol/L.h; Cmax 1.32 (2.06) mmol/L; Tmax 30.0 (300) min; and increase above fasting at 6 h 0.78 (0.62) mmol/L.

This is the most detailed postprandial study performed to date and suggests that identifying predictors of the postprandial 6 h TG rise will have the highest CVD relevance. Ongoing exploration in PREDICT I of the determinants of postprandial lipemic responses considering environmental, genetic and microbiome variables will significantly advance our ability to predict an individual's postprandial response and its links to cardiovascular risk.

Invasive predators have decimated island biodiversity worldwide. Rats (Rattus spp.) are perhaps the greatest conservation threat to island fauna. The ground nesting Palau Micronesian Scrubfowl Megapodius laperouse senex (Megapodiidae) inhabits many of the islands of Palau’s Rock Island Southern Lagoon Conservation Area (RISL) in the western Pacific. These islands are also heavily visited by tourists and support populations of introduced rats, both of which may act as added stressors for the scrubfowl. Using passive chew-tag and call playback surveys on five tourist-visited and five tourist-free islands, we investigated if rats and tourists negatively affect scrubfowl, and if higher rat activity is associated with tourist presence. Rat detection probability and site occupancy were significantly higher on tourist visited (89% and 99%, respectively) compared to tourist-free islands (52% and 73%). Scrubfowl were detected at significantly more stations on tourist-free (93%) than tourist visited (47%) islands and their relative abundance was higher (2.66 and 1.58 birds per station, respectively), although not statistically significantly. While rat occupancy probability likewise had a non-significant negative effect on scrubfowl numbers across islands, our results show a negative relationship between tourist presence and scrubfowl in the RISL. Our findings also suggest that rat populations may be augmented by tourist visitation in the RISL. Although this situation may not seriously affect the scrubfowl, it may be highly detrimental to populations of other threatened island landbirds.

Alongside the challenge of maintaining economic competitiveness in the face of great uncertainty, Brexit brings an opportunity for the government to set out a new industrial strategy. The case for doing so rests on the need to address areas of persistent structural weakness in the UK economy, including low productivity. But it is important that any new industrial strategy be based on appropriately granular data reflecting the real structure of the UK corporate sector: the overwhelmingly preponderant role of services as opposed to manufacturing, for example; the importance of young, fast-growing firms as opposed to SMEs; the relatively high failure rate of companies in the UK; and the relative lack of successful mid-sized firms. Such a data-driven approach might spawn an industrial strategy quite different from the piecemeal programmes of recent years.

Internationally, the UK is a laggard in this area, and the recently-created Industrial Strategy Council does not look strong enough to change that position. To move forward, the government needs to make industrial strategy a central plank of economic policy, embedded at the heart of the administration with its own staff and funding, and operations based on a comprehensive review of the economic contribution and potential of various types of firm. Needless to say, it cannot be a substitute for a continuing commitment to competition and markets, or a stalking horse for protectionism: interventions should be justified by carefully-argued market failure arguments, be time-limited, and transparently evaluated.

To understand how exposure to victimization during adolescence and the presence of comorbid psychological conditions influence substance use treatment entry and substance use disorder diagnosis from 14 to 25 years old among serious juvenile offenders, this study included 1,354 serious juvenile offenders who were prospectively followed over 7 years. Growth mixture modeling was used to assess profiles of early victimization during adolescence (14–17 years). Discrete time survival mixture analysis was used to assess time to treatment entry and substance use disorder diagnosis. Posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) were used as predictors of survival time. Mixture models revealed three profiles of victimization: sustained poly-victimization, moderate/decreasing victimization, and low victimization. Youth in the sustained poly-victimization class were more likely to enter treatment earlier and have a substance use diagnosis earlier than other classes. PTSD was a significant predictor of treatment entry for youth in the sustained poly-victimization class, and MDD was a significant predictor of substance use disorder diagnosis for youth in the moderate/decreasing victimization class. Therefore, substance use prevention programming targeted at youth experiencing poly-victimization in early adolescence—especially those who have PTSD or MDD—is needed.

Cognitive-behavioural therapy (CBT) is an effective treatment for depressed adults. CBT interventions are complex, as they include multiple content components and can be delivered in different ways. We compared the effectiveness of different types of therapy, different components and combinations of components and aspects of delivery used in CBT interventions for adult depression. We conducted a systematic review of randomised controlled trials in adults with a primary diagnosis of depression, which included a CBT intervention. Outcomes were pooled using a component-level network meta-analysis. Our primary analysis classified interventions according to the type of therapy and delivery mode. We also fitted more advanced models to examine the effectiveness of each content component or combination of components. We included 91 studies and found strong evidence that CBT interventions yielded a larger short-term decrease in depression scores compared to treatment-as-usual, with a standardised difference in mean change of −1.11 (95% credible interval −1.62 to −0.60) for face-to-face CBT, −1.06 (−2.05 to −0.08) for hybrid CBT, and −0.59 (−1.20 to 0.02) for multimedia CBT, whereas wait list control showed a detrimental effect of 0.72 (0.09 to 1.35). We found no evidence of specific effects of any content components or combinations of components. Technology is increasingly used in the context of CBT interventions for depression. Multimedia and hybrid CBT might be as effective as face-to-face CBT, although results need to be interpreted cautiously. The effectiveness of specific combinations of content components and delivery formats remain unclear. Wait list controls should be avoided if possible.

Social network characteristics of people who inject drugs (PWID) have previously been flagged as potential risk factors for HCV transmission such as increased injection frequency. To understand the role of the injecting network on injection frequency, we investigated how changes in an injecting network over time can modulate injecting risk behaviour. PWID were sourced from the Networks 2 Study, a longitudinal cohort study of PWID recruited from illicit drug street markets across Melbourne, Australia. Network-related correlates of injection frequency and the change in frequency over time were analysed using adjusted Cox Proportional Hazards and Generalised Estimating Equations modelling. Two-hundred and eighteen PWID followed up for a mean (s.d.) of 2.8 (1.7) years were included in the analysis. A greater number of injecting partners, network closeness centrality and eigenvector centrality over time were associated with an increased rate of infection frequency. Every additional injection drug partner was associated with an increase in monthly injection frequency. Similarly, increased network connectivity and centrality over time was also associated with an increase in injection frequency. This study observed that baseline network measures of connectivity and centrality may be associated with changes in injection frequency and, by extension, may predict subsequent HCV transmission risk. Longitudinal changes in network position were observed to correlate with changes in injection frequency, with PWID who migrate from the densely-connected network centre out to the less-connected periphery were associated with a decreased rate of injection frequency.

NHS Foundation Trust (FT) hospitals in England have complex internal governance arrangements. They may be considered to exhibit meta-regulatory characteristics to the extent that governors are able to promote deliberative values and steer internal governance processes towards wider regulatory goals. Yet, while recent studies of NHS FT hospital governance have explored FT governors and examined FT hospital boards to consider executive oversight, there is currently no detailed investigation of interactions between these two groups. Drawing on observational and interview data from four case-study sites, we trace interactions between the actors involved; explore their understandings of events; and consider the extent to which the proposed benefits of meta-regulation were realised in practice. Findings show that while governors provided both a conscience and contribution to internal and external governance arrangements, the meta-regulatory role was largely symbolic and limited to compliance and legitimation of executive actions. Thus while the meta-regulatory ‘architecture’ for governor involvement may be considered effective, the soft intelligence gleaned and operationalised may be obscured by ‘hard’ performance metrics which dominate resource-allocation processes and priority-setting. Governors were involved in practices that symbolised deliberative involvement but resulted in further opportunities for legitimising executive decisions.

The role of basal melting within high-precipitation temperate glaciers represents a significant gap in understanding glacier melting processes. We use a basal melt equation to calculate geothermal and frictional heat-induced basal melt and develop an equation to calculate the rainfall-induced basal melt for Franz Josef Glacier, New Zealand, a high-precipitation, temperate glacier. Additionally, we calculate basal melt due to heat dissipation within water and ice. Data collated from published information on glacier dynamics and climate station readings show that total basal melt contributes on average ∼2.50 m a−1 over the lower to mid-ablation zone (300–1500 m a.s.l.), which is equivalent to >10% of the total ablation for the glacier. This indicates that basal melting is an important component of mass balance for high-precipitation, temperate glaciers.

Here we report a laboratory study of the effects of debris thickness, diurnally cyclic radiation and rainfall on melt rates beneath rock-avalanche debris and sand (representing typical highly permeable supraglacial debris). Under continuous, steady-state radiation, sand cover >50 mm thick delays the onset of ice-surface melting by >12 hours, but subsequent melting matches melt rates of a bare ice surface. Only when diurnal cycles of radiation are imposed does the debris reduce the longterm rate of ice melt beneath it. This is because debris >50 mm thick never reaches a steady-state heat flux, and heat acquired during the light part of the cycle is partially dissipated to the atmosphere during the nocturnal part of the cycle, thereby continuously reducing total heat flux to the ice surface underneath. The thicker the debris, the greater this effect. Rain advects heat from high-permeability supraglacial debris to the ice surface, thereby increasing ablation where thin, highly porous material covers the ice. In contrast, low-permeability rock-avalanche material slows water percolation, and heat transfer through the debris can cease when interstitial water freezes during the cold/night part of the cycle. This frozen interstitial water blocks heat advection to the ice–debris contact during the warm/day part of the cycle, thereby reducing overall ablation. The presence of metre-deep rock-avalanche debris over much of the ablation zone of a glacier can significantly affect the mass balance, and thus the motion, of a glacier. The length and thermal intensity of the diurnal cycle are important controls on ablation, and thus both geographical location and altitude significantly affect the impact of debris on glacial melting rates; the effect of debris cover is magnified at high altitude and in lower latitudes.