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Aims: To describe clinic management and referral pathways among adults with type 1 diabetes (T1D) aged > 25 years attending a public outpatient diabetes service. Methods: Retrospective cohort study of people with T1D aged > 25 years seen by endocrinologists in one Australian urban public outpatient in 2017. Electronic and paper medical records were reviewed using a dataset adapted from the UK National Institute for Health and Care Excellence 2015 guidelines. Results: Among the 111 people with T1D (mean age 41 ± 13 years, 55% men, mean body mass index 27.1 ± 5.6 kg/m2), mean HbA1c was 8.1 ± 1.9% (66 ± 19 mmol/mol) (lower than the Australian National Diabetes Audit: 8.5%/69 mmol/mol) with 25.5% meeting the guideline target of < 53 mmol/mol (7.0%). Most people had seen a diabetes educator (80.2%) or dietitian (73.0%) and had complication screening. Complication rates were high (nephropathy 20.4%, retinopathy 27.4%, peripheral neuropathy 30.1%, ischaemic heart disease/acute infarction 10.5%). Overall, 27% of referrals occurred following an acute inpatient admission or emergency department presentation and 13% for management of diabetes in pregnancy. Conclusions: A high proportion of people with T1D accessed public specialist care either during pregnancy or after a largely avoidable acute glycaemia-related hospital presentation. Subsequent care was in line with national specialist standards. This area has a "wait for acute event" rather than "complication prevention" model of care, associated with under-referral to the local multidisciplinary specialist service. Understanding how widespread this model of care is, and ways to reduce its prevalence, are urgently required. [ABSTRACT FROM AUTHOR]

The UK Biobank is a very large, prospective population-based cohort study across the United Kingdom. It provides unprecedented opportunities for researchers to investigate the relationship between genotypic information and phenotypes of interest. Multiple regression methods, compared with genome-wide association studies (GWAS), have already been showed to greatly improve the prediction performance for a variety of phenotypes. In the high-dimensional settings, the lasso, since its first proposal in statistics, has been proved to be an effective method for simultaneous variable selection and estimation. However, the large-scale and ultrahigh dimension seen in the UK Biobank pose new challenges for applying the lasso method, as many existing algorithms and their implementations are not scalable to large applications. In this paper, we propose a computational framework called batch screening iterative lasso (BASIL) that can take advantage of any existing lasso solver and easily build a scalable solution for very large data, including those that are larger than the memory size. We introduce snpnet, an R package that implements the proposed algorithm on top of glmnet and optimizes for single nucleotide polymorphism (SNP) datasets. It currently supports ℓ1-penalized linear model, logistic regression, Cox model, and also extends to the elastic net with ℓ1/ℓ2 penalty. We demonstrate results on the UK Biobank dataset, where we achieve competitive predictive performance for all four phenotypes considered (height, body mass index, asthma, high cholesterol) using only a small fraction of the variants compared with other established polygenic risk score methods. Author summary: With the advent and evolution of large-scale and comprehensive biobanks, there come up unprecedented opportunities for researchers to further uncover the complex landscape of human genetics. One major direction that attracts long-standing interest is the investigation of the relationships between genotypes and phenotypes. This includes but doesn't limit to the identification of genotypes that are significantly associated with the phenotypes, and the prediction of phenotypic values based on the genotypic information. Genome-wide association studies (GWAS) is a very powerful and widely used framework for the former task, having produced a number of very impactful discoveries. However, when it comes to the latter, its performance is fairly limited by the univariate nature. To address this, multiple regression methods have been suggested to fill in the gap. That said, challenges emerge as the dimension and the size of datasets both become large nowadays. In this paper, we present a novel computational framework that enables us to solve efficiently the entire lasso or elastic-net solution path on large-scale and ultrahigh-dimensional data, and therefore make simultaneous variable selection and prediction. Our approach can build on any existing lasso solver for small or moderate-sized problems, scale it up to a big-data solution, and incorporate other extensions easily. We provide a package snpnet that extends the glmnet package in R and optimizes for large phenotype-genotype data. On the UK Biobank, we observe competitive prediction performance of the lasso and the elastic-net for all four phenotypes considered from the UK Biobank. That said, the scope of our approach goes beyond genetic studies. It can be applied to general sparse regression problems and build scalable solution for a variety of distribution families based on existing solvers. [ABSTRACT FROM AUTHOR]

Background: There is evidence that mental health problems are increasing and substance use behaviours are decreasing. This paper aimed to investigate recent trends in mental ill health and health-related behaviours in two cohorts of UK adolescents in 2005 and 2015.Methods: Prevalences in mental health (depressive symptoms, self-harm, anti-social behaviours, parent-reported difficulties) and health-related behaviours (substance use, weight, weight perception, sleep, sexual intercourse) were examined at age 14 in two UK birth cohorts; Avon Longitudinal Study of Parents and Children (ALSPAC, N = 5627, born 1991-92) and Millennium Cohort Study (MCS, N = 11 318, born 2000-02). Prevalences and trend estimates are presented unadjusted and using propensity score matching and entropy balancing to account for differences between samples.Results: Depressive symptoms (9% to 14.8%) and self-harm (11.8% to 14.4%) were higher in 2015 compared with 2005. Parent-reported emotional difficulties, conduct problems, hyperactivity and peer problems were higher in 2015 compared with 2005 (5.7-8.9% to 9.7-17.7%). Conversely, substance use (tried smoking, 9.2% to 2.9%; tried alcohol, 52.1% to 43.5%, cannabis, 4.6% to 3.9%), sexual activity (2% to 0.9%) and anti-social behaviours (6.2-40.1% to 1.6-27.7%) were less common or no different. Adolescents in 2015 were spending less time sleeping (<8 h 5.7% to 11.5%), had higher body mass index (BMI) (obese, 3.8% to 7.3%) and a greater proportion perceived themselves as overweight (26.5% to 32.9%). The findings should be interpreted bearing in mind limitations in ability to adequately harmonize certain variables and account for differences in attrition rates and generalizability of the two cohorts.Conclusions: Given health-related behaviours are often cited as risk factors for poor mental health, our findings suggest relationships between these factors might be more complex and dynamic in nature than currently understood. Substantial increases in mental health difficulties, BMI and poor sleep-related behaviours highlight an increasing public health challenge. [ABSTRACT FROM AUTHOR]

Objective The main aim of this study was to explore any association between colorectal cancer (CRC) and rotating shift work in nurses and midwives. The secondary aim of this study was to identify risk factors for CRC in nurses and midwives who are rotating shift workers. Design Cross-sectional study. Setting Electronic survey of participants from Australia, New Zealand and the United Kingdom. Subjects A sample of 8,199 male and female nurses and midwives from Australia, New Zealand and the United Kingdom invited through their registration papers or newsletter advertisement. Main outcome measure Prevalence of CRC in participants who have worked rotating shifts for 1-14 years or ≥15 years compared to participants who have never worked rotating shifts. In addition, risk factors for CRC in the rotating shift work population were analysed in a multivariate logistic regression model in order to obtain odds ratio of CRC. Results No statistically significant difference was found in the prevalence of CRC between those who have never worked rotating shift work, worked 1-14 years and worked 15 or more years. Among rotating shift workers, diabetes was associated with a 123-fold (95% CI 39-392; p<0.001) increased odds of CRC, while Inflammatory Bowel Disease (IBD) was associated with a 190-fold (95%CI 68-526; p<0.001) increased odds of CRC. Screening colonoscopy or sigmoidoscopy for CRC was associated with a 10-fold (95% CI 3-35; p<0.001) increased odds of being diagnosed with CRC. Conclusion No significant association was found between rotating shift work and colorectal cancer in nurses and midwives. In nurses and midwives who are rotating shift workers, diabetes, IBD and CRC screening significantly increased the odds of CRC. [ABSTRACT FROM AUTHOR]

Missing data are a commonly occurring threat to the validity and efficiency of epidemiologic studies. Perhaps the most common approach to handling missing data is to simply drop those records with 1 or more missing values, in so-called "complete records" or "complete case" analysis. In this paper, we bring together earlier-derived yet perhaps now somewhat neglected results which show that a logistic regression complete records analysis can provide asymptotically unbiased estimates of the association of an exposure of interest with an outcome, adjusted for a number of confounders, under a surprisingly wide range of missing-data assumptions. We give detailed guidance describing how the observed data can be used to judge the plausibility of these assumptions. The results mean that in large epidemiologic studies which are affected by missing data and analyzed by logistic regression, exposure associations may be estimated without bias in a number of settings where researchers might otherwise assume that bias would occur. [ABSTRACT FROM AUTHOR]

Background: Parents typically do not recognise their child's weight status accurately according to clinical criteria, and thus may not take appropriate action if their child is overweight. We developed a novel visual intervention designed to improve parental perceptions of child weight status according to clinical criteria for children aged 4-5 and 10-11 years. The Map Me intervention comprises age- and sex-specific body image scales of known body mass index and supporting information about the health risks of childhood overweight. Design: This cluster randomised trial will test the effectiveness of the Map Me intervention. Primary schools will be randomised to: paper-based Map Me; web-based Map Me; no information (control). Parents of reception (4-5 years) and year 6 (10-11 years) children attending the schools will be recruited. The study will work with the National Child Measurement Programme which measures the height and weight of these year groups and provides feedback to parents about their child's weight status. Before receiving the feedback, parents will complete a questionnaire which includes assessment of their perception of their child's weight status and knowledge of the health consequences of childhood overweight. The control group will provide pre-intervention data with assessment soon after recruitment; the intervention groups will provide post-intervention data after access to Map Me for one month. The study will subsequently obtain the child height and weight measurements from the National Child Measurement Programme. Families will be followed-up by the study team at 12 months. The primary outcome is any difference in accuracy in parental perception of child weight status between pre-intervention and post-intervention at one month. The secondary outcomes include differences in parent knowledge, intention to change lifestyle behaviours and/or seek advice or support, perceived control, action planning, coping planning, and child weight status at 12 month follow-up. Discussion: The Map Me tool has potential to make a positive impact on children's health at a population level by introducing it into current intervention programmes to improve accuracy of parental perception of child's weight status. This trial will inform the action of researchers, educators, health professionals and policy makers. [ABSTRACT FROM AUTHOR]

New pre-school UK charts have been produced incorporating the new World Health Organization growth standards based on healthy breastfed infants. This paper describes the process by which the charts and evidence-based instructions were designed and evaluated, and what it revealed about professional understanding of charts and growth monitoring. A multidisciplinary expert group drew on existing literature, new data analyses and parent focus groups as well as two series of chart-plotting workshops for health staff. The first series explored possible design features and general chart understanding. The second evaluated an advanced prototype with instructions, using plotting and interpretation of three separate scenarios on the old charts, compared with the new charts. The first plotting workshops (46 participants) allowed decisions to be made about the exact chart format, but it also revealed widespread confusion about use of adjustment for gestation and the plotting of birthweight. In the second series (78 participants), high levels of plotting inaccuracy were identified on both chart formats, with 64% of respondents making at least one major mistake. Significant neonatal weight loss was poorly recognized. While most participants recognized abnormal and normal growth patterns, 13-20% did not. Many respondents had never received any formal training in chart use. Growth charts are complex clinical tools that are, at present, poorly understood and inconsistently used. The importance of clear guidelines and formal training has now been recognized and translated into supporting educational materials (free to download at ). [ABSTRACT FROM AUTHOR]

This paper investigates the proposition made by contemporaries that women and children disproportionately bore the brunt of industrialisation and urbanisation by examining how poor working-class families in mid-Victorian London shared their resources. Allocation is inferred from independently pooled cross-sectional data on the height, weight and body mass of 32,584 prisoners from a London House of Correction. As boys and girls moved into adulthood, they made some biological gains consistent with ‘catch up’ on earlier deprivation. The body masses of women and men then diverged. When families grew, women shrank. When children left home taking their wages with them, when age reduced the earning capacities of herself and her husband, women suffered even more, becoming dangerously underweight in older age. Ageing was a gendered experience. [Copyright &y& Elsevier]

Background: Physical activity is associated with improved physical and mental health among children. However, physical activity declines and sedentary time increases with age, and large proportions of older children do not meet the recommended hour per day of moderate-to-vigorous-intensity physical activity (MVPA). The aim of this paper is to identify profiles of children based on the complex relationship between physical activity and sedentary time at ages 6 and 9 and explore how those profiles are associated with other covariates and how they change over time. Methods: Valid accelerometer data were collected for 1132 children aged 6 and 1121 at age 9, with 565 children with data at both ages. We calculated the proportions of total wear time spent in sedentary, light and MVPA activity on both weekdays and weekends. Latent profile (class) analysis was applied separately to the two age groups to identify activity profiles. We then used latent transition analysis to explore transitions between profiles at the two time points. Results: We identified five profiles of activity at age 6 and six profiles at age 9. Although profiles were not directly equivalent, five classes captured similar patterns at both ages and ranged from very active to inactive. At both ages, active profiles, where the majority achieved the recommended MVPA guidelines, were more likely to be active at weekends than on weekdays. There was substantial movement between classes, with strongest patterns of movement to classes with no change or a decrease in MVPA. Transition between classes was associated with sex, BMI z-score, screen-viewing and participation in out-of-school activities. Conclusions: This paper is the first to apply latent profile analysis to the physical activity of UK children as they move through primary school. Profiles were identified at ages 6 and 9, reflecting different weekday and weekend patterns of physical activity and sedentary time. There was substantial movement between profiles between ages 6 and 9, mostly to no change or less active profiles. Weekend differences suggest that greater focus on how weekend activity contributes to an average of 60 min per day of MVPA across the week may be warranted. [ABSTRACT FROM AUTHOR]

Background: Physical activity is associated with improved physical and mental health among children, but many children do not meet the recommended hour per day of moderate-to-vigorous-intensity physical activity (MVPA). The aim of this paper is to investigate participation in active after-school clubs and active travel to and from school at age 11 and estimate the average daily minutes of MVPA associated with active club attendance and active travel. Methods: Accelerometer data were collected on three weekdays for 1296 11-year-old children in a cross-sectional study. Children reported attendance at active after-school clubs and how they travelled to and from school for each day of the week. To account for repeat days within child and clustering within schools we used multilevel models with random effects at the school and child level, and fixed effects for all covariates. We calculated odds ratios for participation in active after-school clubs and active travel for gender, measures of socio-economic position and BMI category. We also explored the association between active club attendance, active travel and daily average MVPA. Results: Boys and girls were equally likely to attend active after-school clubs. Boys were more likely to travel to school using active modes. Attendance at active after-school clubs and active travel home were not associated with each other. Attending an active after-school club was associated with an additional 7.6 min (95% CI: 5.0 to 10.3) average MVPA on that day among both boys and girls. Active travel was associated with an additional 4.7 min (95% CI: 2.9 to 6.5) average MVPA per journey for boys and 2.4 min (95% CI: 1.0 to 3.7) for girls. Conclusions: Both active after-school clubs and active travel are associated with greater physical activity on the day that children participate in these, and we saw no evidence that those attending active clubs do so at the expense of active travel home afterwards. While the increased daily MVPA is small to moderate, active after-school clubs and active travel on multiple days of the week could make important contributions as part of complex interventions aimed at increasing population levels of physical activity in children. [ABSTRACT FROM AUTHOR]