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Introduction\udStudies of prevalence and the demographic profile of type 1 diabetes are challenging because of the relative rarity of the condition, however, these outcomes can be determined using routine healthcare data repositories. Understanding the epidemiology of type 1 diabetes allows for targeted interventions and care of this life-affecting condition.\ud\ud\udObjectives\udTo describe the prevalence, incidence and demographics of persons with type 1 diabetes diagnosed in Wales, UK, using the Secure Anonymised Information Linkage (SAIL) Databank.\ud\ud\udMethods\udData derived from primary and secondary care throughout Wales available in the SAIL Databank were used to identify people with type 1 diabetes to determine the prevalence and incidence of type 1 diabetes over a 10 year period (2008–18) and describe the demographic and clinical characteristics of this population by age, socioeconomic deprivation and settlement type. The seasonal variation in incidence rates was also examined.\ud\ud\udResults\udThe prevalence of type 1 diabetes in 2018 was 0.32% in the whole population, being greater in men compared to women (0.35% vs 0.28% respectively); highest in those aged 15-29 years (0.52%) and living in the most socioeconomically deprived areas (0.38%). The incidence of type 1 diabetes over 10 years was 14.0 cases/100,000 people/year for the whole population of Wales. It was highest in children aged 0-14 years (33.6 cases/100,000 people/year) and areas of high socioeconomic deprivation (16.8 cases/100,000 people/year) and least in those aged 45-60 years (6.5 cases/100,000 people/year) and in areas of low socioeconomic deprivation (11.63 cases/100,000 people/year). A seasonal trend in the diagnoses of type 1 diabetes was observed with higher incidence in winter months.\ud\ud\udConclusion\udThis nation-wide retrospective epidemiological study using routine data revealed that the incidence of type 1 diabetes in Wales was greatest in those aged 0-14 years with a higher incidence and prevalence in the most deprived areas. These findings illustrate the need for health-related policies targeted at high deprivation areas to include type 1 diabetes in their remit.

AIMS To understand factors associated with repeat non-attendance at screening for diabetes-related retinopathy. METHODS Retrospective observational study using anonymised data from Diabetic Eye Screening Wales for people with a full history of screening invitations and attendances was linked with primary and secondary care records held in the Secure Anonymised Information Linkage Databank. Repeat non-attendance was defined as no record of attendance during any 36-month period despite three cycles of annual screening invitations. The associations between repeat non-attendance and potential risk factors were examined using multivariable logistic regression analysis, stratified according to type 1 and type 2 diabetes. RESULTS A total of 18% with type 1 diabetes (1146/6513) and 8% with type 2 diabetes (12,475/156,525) were repeat non-attenders. Participants attending their very first appointment were least likely to become repeat non-attenders [odds ratio (95% confidence interval)]: type 1 diabetes: 0.12 (0.09, 0.17) and type 2 diabetes: 0.08 (0.07, 0.09). For both types of diabetes, those of a younger age, living in areas of higher deprivation and subject to multiple house moves were at greater risk of becoming repeat non-attenders. CONCLUSION/INTERPRETATION A more tailored approach is needed for the younger population, those living in areas of higher deprivation and/or undergoing multiple residential relocation and to ensure attendance at their initial appointment to minimise future repeat non-attendance.

IntroductionMulti-morbidity is a widely recognised but poorly understood global issue that appears to be increasing in prevalence, according to the UK’s Academy of Medical Sciences (AMS) report in 2018. Disease clustering, their determinants and consequences are poorly researched. Better understanding would help drive prevention and improved clinical care, services and patient outcomes. Objectives and ApproachDevelopment of two comprehensive population-wide e-cohorts, derived utilising data linkage techniques and including multi-sourced anonymised routine health and demographic data held within the SAIL Databank. The objective is to characterise multi-morbidity and its clustering, determinants and outcomes and compare methods using a) prospective cohort design using multiple data sources in Wales and b) retrospective cohort design to examine household level and environment clustering using GP data in demographically diverse populations (Wales and North East London). The prospective e-cohort focuses on adults living in Wales on 1 st January 2000 and followed up to 2020, including data from the NHS population register, deaths, inpatients, outpatients, Emergency Department, GP, disease registries, laboratory data, and population surveys with QoL measures. This e-cohort will be harmonised with other sites across the UK. The retrospective e-cohort is designed to harmonise with a North East London e-cohort, including all individuals living in Wales on 24 th April 2018 and registered with a GP. Results2.8 and 2.2 million individuals have been included in the prospective and retrospective cohorts respectively, with 43.6 million person years of follow up. Established comorbidity indices and published phenotypes from libraries are being applied to the data to create initial prevalence and incidence estimates for further analysis. Important clusters will be determined by associations with mortality and excess healthcare utilisation. Conclusion/ImplicationsBuilding the e-cohorts has involved multiple disciplines across organisations. Multi-morbidity prevalence estimates and study designs will be compared prior to statistical analyses and machine learning methods to evaluate clustering and determinants.

OBJECTIVE: To determine the risk factors for having diabetic retinopathy (DR) in children and young people (CYP) with type 1 diabetes (T1DM) at first screening. METHODS: Records from the Diabetes Eye Screening Wales (DESW) service for people in Wales, UK, with T1DM diagnosed under age 18 years were combined with other electronic health record (EHR) data in the Secure Anonymised Information Linkage (SAIL) Databank. Data close to the screening date were collected, and risk factors derived from multivariate, multinomial logistic regression modelling. RESULTS: Data from 4172 persons, with median (lower quartile, upper quartile) age 16.3 (13.0, 22.3) years and duration of diabetes 6.6 (2.3, 12.3) years were analysed. 62.6% (n = 2613) had no DR, 26.7% (n = 1112) background DR, and 10.7% (n = 447) had referable DR (RDR). No RDR was observed under 19 years of age. Factors associated with an increased risk of DR were diabetes duration, elevated HbA(1c), and diastolic blood pressure. People diagnosed with T1DM at 12 years or older had an additional risk for each year they had diabetes compared to those diagnosed before age 12 controlling for the diabetes duration (odds ratios 1.23 and 1.34, respectively). CONCLUSIONS: This study found that 37.4% of the study cohort had DR at first screening, the risk being greater the longer the duration of diabetes or higher the HbA(1c) and diastolic blood pressure. In addition, people diagnosed at 12 years of age or over were more likely to have DR with each additional year with diabetes.

ObjectivesTo investigate concussion injury rates, the likelihood of sustaining concussion relative to the number of rugby union matches and the risk of subsequent injury following concussion.MethodsA four-season (2012/2013–2015/2016) prospective cohort study of injuries in professional level (club and international) rugby union. Incidence (injuries/1000 player-match-hours), severity (days lost per injury) and number of professional matches conferring a large risk of concussion were determined. The risk of injury following concussion was assessed using a survival model.ResultsConcussion incidence increased from 7.9 (95% CI 5.1 to 11.7) to 21.5 injuries/1000 player-match-hours (95% CI 16.4 to 27.6) over the four seasons for combined club and international rugby union. Concussion severity was unchanged over time (median: 9 days). Players were at a greater risk of sustaining a concussion than not after an exposure of 25 matches (95% CI 19 to 32). Injury risk (any injury) was 38% greater (HR 1.38; 95% CI 1.21 to 1.56) following concussion than after a non-concussive injury. Injuries to the head and neck (HR 1.34; 95% CI 1.06 to 1.70), upper limb (HR 1.59; 95% CI 1.19 to 2.12), pelvic region (HR 2.07; 95% CI 1.18 to 3.65) and the lower limb (HR 1.60; 95% CI 1.21 to 2.10) were more likely following concussion than after a non-concussive injury.ConclusionConcussion incidence increased, while severity remained unchanged, during the 4 years of this study. Playing more than 25 matches in the 2015/2016 season meant that sustaining concussion was more likely than not sustaining concussion. The 38% greater injury risk after concussive injury (compared with non-concussive injury) suggests return to play protocols warrant investigation.

Artificial rugby union playing surface installation is increasing. This prospective cohort study aimed to examine the effect of playing surface on match injury types within 157 players of two UK professional rugby union clubs playing 209 matches (96 on artificial surfaces and 113 on grass) over three seasons. There was no difference in overall injury risk between the two playing surfaces with injury incidence on artificial 80.2 (CI 69.9-91.7) and on grass 81.9 per 1000 match-hours (CI 72.2-92.5), with an incidence rate ratio (RR) of 0.98 (CI 0.82-1.17). There was a higher rate of concussion (RR 0.52, CI 0.34 - 0.78) and chest injuries on grass (RR 0.26 CI 0.07, 0.95), and a higher rate of thigh haematoma (RR 2.25, CI 1.05-4.82) foot injuries (RR 4.12, CI 1.10, 15.40) and injury to players being tackled (RR 1.46, CI 1.00, 2.15) on artificial. Whilst there was no higher injury risk for matches played on artificial versus natural grass surfaces, the higher incidence of concussion and chest injury on grass, and the higher rate of foot injuries on artificial surfaces may be related to tackle and footwear-to-surface interface factors.

IntroductionType 1 diabetes mellitus (T1DM) is an autoimmune condition characterised by hyperglycaemia, caused by the destruction of insulin producing β-cells in the pancreas. Previous epidemiological population level studies of T1DM and its complications have typically used recorded T1DM diagnoses to determine diabetes status and define cohorts. Objectives and ApproachThe objective was to identify all persons with T1DM in Wales from Primary (~70\% population coverage) and Secondary Care (100% coverage) data held in the Secure Anonymised Information Linkage (SAIL) databank. People with a coded T1DM diagnosis (using Read codes in Primary Care data and International Classification of Disease (ICD10) codes in Secondary Care data), plus either insulin prescribed shortly after diagnosis or a hospital admission for diabetic ketoacidosis were identified as having T1DM. A sub-group of this SAIL e-cohort were validated using a register of persons diagnosed with T1DM in Wales under 15 years old (Brecon cohort). Results18,285 people had a T1DM diagnosis and 10,539 had more T1DM than type 2 diabetes mellitus (T2DM) diagnoses. 6,375 persons were identified with T1DM in Primary Care data using our criteria, with a median diagnosis age of 19.2 years (interquartile range 11.0, 35.5). 47.5\% were diagnosed under 18 years of age. 39.6% of people with a T1DM diagnosis did not have T1DM using our criteria. False positive and negative rates of 4.8% and 4.5% respectively were achieved by comparing persons in the SAIL e-cohort against the Brecon cohort. Clinician estimated false positive and negative rates were 1.4% and 3.9% respectively. The prevalence of T1DM in Wales in 2016 was 0.37% or 11,049 people. Conclusion/ImplicationsOur criteria for identifying people with T1DM was more reliable than using diagnosis codes alone, allowing for a more accurate, efficient and reproducible means of identifying individuals with T1DM for researchers utilising the SAIL databank, and other national health repositories.

There has been much discussion over many years of the alleged gap between management accounting theory and practice. Whereas researchers sympathetic to neoclassical economics have sought to rationalise the gap in terms of information economics and have proposed constrained optimisation as the norm, Scapens [Scapens, R.W., 1994. Never mind the gap: towards an institutional perspective of management accounting practices. Management Accounting Research 5, 302–321.] and Ahmed and Scapens [Ahmed, M.N., Scapens, R.W., 2000. Cost allocation in Britain: towards an institutional analysis. European Accounting Review 9(2), 159–204.] have suggested that the old institutional economics framework might better explain management accounting practice. Old institutional economics originated at about the same time as neoclassical economics and in opposition to it. It largely rejects rational optimisation as the basis for much of human behaviour and stresses instead, adherence to custom and rule following. This paper reports on the findings of two cases studies undertaken as part of a programme of case studies, intended to examine the power of the old institutional economics framework to explain the gap between management accounting theory and practice, in the realm of costing for pricing. The paper concludes that much observed management accounting practice is difficult to reconcile with ex ante constrained optimisation, but is explicable in terms of conditioning by various institutions.

This article examines the relationship between manufacturing strategy and self-managed work teams. The implementation of team working and lean production as a strategy designed to improve competitive advantage is juxtaposed with the cultural influences utilizing Hofstede's (1980) model of cultural dimensions. Case study material illustrates the contrasting positions posed by the different cultural and philosophical bases of lean production and self-managed work teams. The resulting disequilibrium may explain why the implementation of teams in Western environments has proved less successful than anticipated in the manufacturing context. © 2001 John Wiley & Sons, Inc.