10-year multimorbidity patterns among people with and without rheumatic and musculoskeletal diseases: an observational cohort study using linked electronic health records from Wales, UK.

OBJECTIVES: To compare the patterns of multimorbidity between people with and without rheumatic and musculoskeletal diseases (RMDs) and to describe how these patterns change by age and sex over time, between 2010 and 2019. PARTICIPANTS: 103 426 people with RMDs and 2.9 million comparators registered in 395 Wales general practices (GPs). Each patient with an RMD aged 0-100 years between January 2010 and December 2019 registered in Clinical Practice Research Welsh practices was matched with up to five comparators without an RMD, based on age, gender and GP code. PRIMARY OUTCOME MEASURES: The prevalence of 29 Elixhauser-defined comorbidities in people with RMDs and comparators categorised by age, gender and GP practices. Conditional logistic regression models were fitted to calculate differences (OR, 95% CI) in associations with comorbidities between cohorts. RESULTS: The most prevalent comorbidities were cardiovascular risk factors, hypertension and diabetes. Having an RMD diagnosis was associated with a significantly higher odds for many conditions including deficiency anaemia (OR 1.39, 95% CI (1.32 to 1.46)), hypothyroidism (OR 1.34, 95% CI (1.19 to 1.50)), pulmonary circulation disorders (OR 1.39, 95% CI 1.12 to 1.73) diabetes (OR 1.17, 95% CI (1.11 to 1.23)) and fluid and electrolyte disorders (OR 1.27, 95% CI (1.17 to 1.38)). RMDs have a higher proportion of multimorbidity (two or more conditions in addition to the RMD) compared with non-RMD group (81% and 73%, respectively in 2019) and the mean number of comorbidities was higher in women from the age of 25 and 50 in men than in non-RMDs group. CONCLUSION: People with RMDs are approximately 1.5 times as likely to have multimorbidity as the general population and provide a high-risk group for targeted intervention studies. The individuals with RMDs experience a greater load of coexisting health conditions, which tend to manifest at earlier ages. This phenomenon is particularly pronounced among women. Additionally, there is an under-reporting of comorbidities in individuals with RMDs.

Children and young people's body mass index measures derived from routine data sources: A national data linkage study in Wales.

BACKGROUND: Routine monitoring of Body Mass Index (BMI) in general practice, and via national surveillance programmes, is essential for the identification, prevention, and management of unhealthy childhood weight. We examined and compared the presence and representativeness of children and young people's (CYPs) BMI recorded in two routinely collected administrative datasets: general practice electronic health records (GP-BMI) and the Child Measurement Programme for Wales (CMP-BMI), which measures height and weight in 4-5-year-old school children. We also assessed the feasibility of combining GP-BMI and CMP-BMI data for longitudinal analyses. METHODS: We accessed de-identified population-level GP-BMI data for calendar years 2011 to 2019 for 246,817 CYP, and CMP-BMI measures for 222,772 CYP, held within the Secure Anonymised Information Linkage Databank. We examined the proportion of CYP in Wales with at least one GP-BMI record, its distribution by child socio-demographic characteristics, and trends over time. We compared GP-BMI and CMP-BMI distributions. We quantified the proportion of children with a CMP-BMI measure and a follow-up GP-BMI recorded at an older age and explored the representativeness of these measures. RESULTS: We identified a GP-BMI record in 246,817 (41%) CYP, present in a higher proportion of females (54.2%), infants (20.7%) and adolescents. There was no difference in the deprivation profile of those with a GP-BMI measurement. 31,521 CYP with a CMP-BMI had at least one follow-up GP-BMI; those with a CMP-BMI considered underweight or very overweight were 87% and 70% more likely to have at least one follow-up GP-BMI record respectively compared to those with a healthy weight, as were males and CYP living in the most deprived areas of Wales. CONCLUSIONS: Records of childhood weight status extracted from general practice are not representative of the population and are biased with respect to weight status. Linkage of information from the national programme to GP records has the potential to enhance discussions around healthy weight at the point of care but does not provide a representative estimate of population level weight trajectories, essential to provide insights into factors determining a healthy weight gain across the early life course. A second CMP measurement is required in Wales.

A scoping review finds a growing trend in studies validating multimorbidity patterns and identifies five broad types of validation methods.

OBJECTIVES: Multimorbidity, the presence of two or more long-term conditions, is a growing public health concern. Many studies use analytical methods to discover multimorbidity patterns from data. We aimed to review approaches used in published literature to validate these patterns. STUDY DESIGN AND SETTING: We systematically searched PubMed and Web of Science for studies published between July 2017 and July 2023 that used analytical methods to discover multimorbidity patterns. RESULTS: Out of 31,617 studies returned by the searches, 172 were included. Of these, 111 studies (64%) conducted validation, the number of studies with validation increased from 53.13% (17 out of 32 studies) to 71.25% (57 out of 80 studies) in 2017-2019 to 2022-2023, respectively. Five types of validation were identified: assessing the association of multimorbidity patterns with clinical outcomes (n = 79), stability across subsamples (n = 26), clinical plausibility (n = 22), stability across methods (n = 7) and exploring common determinants (n = 2). Some studies used multiple types of validation. CONCLUSION: The number of studies conducting a validation of multimorbidity patterns is clearly increasing. The most popular validation approach is assessing the association of multimorbidity patterns with clinical outcomes. Methodological guidance on the validation of multimorbidity patterns is needed.

Using hypergraphs to quantify importance of sets of diseases by healthcare resource utilisation: A retrospective cohort study.

Rates of Multimorbidity (also called Multiple Long Term Conditions, MLTC) are increasing in many developed nations. People with multimorbidity experience poorer outcomes and require more healthcare intervention. Grouping of conditions by health service utilisation is poorly researched. The study population consisted of a cohort of people living in Wales, UK aged 20 years or older in 2000 who were followed up until the end of 2017. Multimorbidity clusters by prevalence and healthcare resource use (HRU) were modelled using hypergraphs, mathematical objects relating diseases via links which can connect any number of diseases, thus capturing information about sets of diseases of any size. The cohort included 2,178,938 people. The most prevalent diseases were hypertension (13.3%), diabetes (6.9%), depression (6.7%) and chronic obstructive pulmonary disease (5.9%). The most important sets of diseases when considering prevalence generally contained a small number of diseases, while the most important sets of diseases when considering HRU were sets containing many diseases. The most important set of diseases taking prevalence and HRU into account was diabetes & hypertension and this combined measure of importance featured hypertension most often in the most important sets of diseases. We have used a single approach to find the most important sets of diseases based on co-occurrence and HRU measures, demonstrating the flexibility of the hypergraph approach. Hypertension, the most important single disease, is silent, underdiagnosed and increases the risk of life threatening co-morbidities. Co-occurrence of endocrine and cardiovascular diseases was common in the most important sets. Combining measures of prevalence with HRU provides insights which would be helpful for those planning and delivering services.

A study evaluating quality of life and factors affecting it before, during and after menopause.

OBJECTIVE: To determine if quality of life (QoL) changes before, during and after menopause and whether these changes are linked to symptoms, demographics, and/or lifestyle factors. METHODS: We undertook a cross-sectional online survey. We invited women aged between 35 and 60 years to complete the survey which included the Short-Form 36 (SF-36) generic quality of life measure, the menopause specific Utian-Quality of life (UQOL) measure, and questions about health and wellbeing, menopause symptoms and hormonal stage. The data were analysed with one-way ANOVA analysis and multivariate regression modelling. RESULTS: 279 women completed the survey. Most were aged between 51 and 55 years. In the unadjusted analysis there was a tendency for QoL to deteriorate from pre to peri to menopause and then increase slightly post menopause. This was however not significant in multivariate analysis. Multivariate analysis identified that lifestyle factors significantly influenced QoL. Regular exercise resulted in better QoL scores across a number of the UQol and SF-36 sub-scales. Being very overweight and having more menopause symptoms resulted in worse QoL. CONCLUSIONS: Although there was a trend towards worse quality of life in the peri and menopause stages this was not significantly different in adjusted multivariate analyses. Those experiencing more symptoms had significantly worse QoL. Lifestyle factors may affect QoL, but the picture is not straightforward. It is promising that there was a trend toward improved QoL in the post-menopausal stage. These findings should inform education material and promote awareness of the menopause and its impact on QoL. (245).

Trajectories in chronic disease accrual and mortality across the lifespan in Wales, UK (2005-2019), by area deprivation profile: linked electronic health records cohort study on 965,905 individuals.

Background: Understanding and quantifying the differences in disease development in different socioeconomic groups of people across the lifespan is important for planning healthcare and preventive services. The study aimed to measure chronic disease accrual, and examine the differences in time to individual morbidities, multimorbidity, and mortality between socioeconomic groups in Wales, UK. Methods: Population-wide electronic linked cohort study, following Welsh residents for up to 20 years (2000-2019). Chronic disease diagnoses were obtained from general practice and hospitalisation records using the CALIBER disease phenotype register. Multi-state models were used to examine trajectories of accrual of 132 diseases and mortality, adjusted for sex, age and area-level deprivation. Restricted mean survival time was calculated to measure time spent free of chronic disease(s) or mortality between socioeconomic groups. Findings: In total, 965,905 individuals aged 5-104 were included, from a possible 2.9 m individuals following a 5-year clearance period, with an average follow-up of 13.2 years (12.7 million person-years). Some 673,189 (69.7%) individuals developed at least one chronic disease or died within the study period. From ages 10 years upwards, the individuals living in the most deprived areas consistently experienced reduced time between health states, demonstrating accelerated transitions to first and subsequent morbidities and death compared to their demographic equivalent living in the least deprived areas. The largest difference were observed in 10 and 20 year old males developing multimorbidity (-0.45 years (99% CI: -0.45, -0.44)) and in 70 year old males dying after developing multimorbidity (-1.98 years (99% CI: -2.01, -1.95)). Interpretation: This study adds to the existing literature on health inequalities by demonstrating that individuals living in more deprived areas consistently experience accelerated time to diagnosis of chronic disease and death across all ages, accounting for competing risks. Funding: UK Medical Research Council, Health Data Research UK, and Administrative Data Research Wales.

Health care utilization outcomes in patients with blunt chest wall trauma following discharge from the emergency department: A retrospective, observational data-linkage study.

BACKGROUND: Although much is published reporting clinical outcomes in the patients with blunt chest wall trauma who are admitted to hospital from the ED, less is known about the patients' recovery when they are discharged directly without admission. The aim of this study was to investigate the health care utilization outcomes in adult patients with blunt chest wall trauma, discharged directly from ED in a trauma unit in the United Kingdom. METHODS: This was a longitudinal, retrospective, single-center, observational study incorporating analysis of linked datasets, using the Secure Anonymised Information Linkage databank for admissions to a trauma unit in the Wales, between January 1, 2016, and December 31, 2020. All patients 16 years or older with a primary diagnosis of blunt chest wall trauma discharged directly home were included. Data were analyzed using a negative binomial regression model. RESULTS: There were 3,205 presentations to the ED included. Mean age was 53 years, 57% were male, with the predominant injury mechanism being a low velocity fall (50%). 93% of the cohort sustained between 0 and 3 rib fractures. Four percent of the cohort were reported to have chronic obstructive pulmonary disease, and 4% using preinjury anticoagulants. On regression analysis, inpatient admissions, outpatient appointments and primary care contacts all significantly increased in the 12-week period postinjury, compared with the 12-week period preinjury (odds ratio [OR], 1.63; 95% confidence interval [CI], 1.33-1.99; p < 0.001; OR, 1.28; 95% CI, 1.14-1.43; p < 0.001; OR, 1.02; 95% CI, 1.01-1.02; p < 0.001, respectively). Risk of health care resource utilization increased significantly with each additional year of age, chronic obstructive pulmonary disease and preinjury anticoagulant use (all p < 0.05). Social deprivation and number of rib fracture did not impact outcomes. CONCLUSION: The results of this study demonstrate the need for appropriate signposting and follow-up for patients with blunt chest wall trauma presenting to the ED, not requiring admission to the hospital. LEVEL OF EVIDENCE: Prognostic and Epidemiological; Level IV.

Impact of severe hypoglycaemia requiring hospitalization on mortality in people with type 1 diabetes: A national retrospective observational cohort study.

AIMS: To assess if the risk of all-cause mortality increases in people with type 1 diabetes (T1D) with increasing number of severe hypoglycaemia episodes requiring hospitalization. MATERIALS AND METHODS: We conducted a national retrospective observational cohort study in people with T1D (diagnosed between 2000 and 2018). Clinical, comorbidity and demographic variables were assessed for impact on mortality for people with no, one, two and three or more episodes of severe hypoglycaemia requiring hospitalization. The time to death (all-cause mortality) from the timepoint of the last episode of severe hypoglycaemia was modelled using a parametric survival model. RESULTS: A total of 8224 people had a T1D diagnosis in Wales during the study period. The mortality rate (95% confidence interval [CI]) was 6.9 (6.1-7.8) deaths/ 1000 person-years (crude) and 15.31 (13.3-17.63) deaths/ 1000 person-years (age-adjusted) for those with no occurrence of severe hypoglycaemia requiring hospitalization. For those with one episode of severe hypoglycaemia requiring hospitalization the mortality rate (95% CI) was 24.9 (21.0-29.6; crude) and 53.8 (44.6-64.7) deaths/ 1000 person-years (age-adjusted), for those with two episodes of severe hypoglycaemia requiring hospitalization it was 28.0 (23.1-34.0; crude) and 72.8 (59.2-89.5) deaths/ 1000 person-years (age-adjusted), and for those with three or more episodes of severe hypoglycaemia requiring hospitalization it was 33.5 (30.0-37.3; crude) and 86.3 (71.7-103.9) deaths/ 1000 person years (age-adjusted; P < 0.001). A parametric survival model showed that having two episodes of severe hypoglycaemia requiring hospitalization was the strongest predictor for time to death (accelerated failure time coefficient 0.073 [95% CI 0.009-0.565]), followed by having one episode of severe hypoglycaemia requiring hospitalization (0.126 [0.036-0.438]) and age at most recent episode of severe hypoglycaemia requiring hospitalization (0.917 [0.885-0.951]). CONCLUSIONS: The strongest predictor for time to death was having two or more episodes of severe hypoglycaemia requiring hospitalization.

Educational Attainment and Childhood-Onset Type 1 Diabetes.

OBJECTIVE: To quantify associations of educational outcomes with type 1 diabetes status and glycemic management (HbA1c). RESEARCH DESIGN AND METHODS: This was a record linkage study of schools and higher (college) education data sets linked to national diabetes audits. The population includes all Welsh children attending school between 2009 and 2016, yielding eight academic cohorts with attainment data, including 263,426 children without diabetes and 1,212 children diagnosed with type 1 diabetes. Outcomes include standardized educational attainment for those aged 16 years, higher education participation for those aged ≥18 years, and school absences among those aged 6-16 years. RESULTS: Comparison between children with type 1 diabetes and children without diabetes showed no strong evidence of associations for student attainment (0.001 SD, 95% CI -0.047 to 0.049, P < 0.96, n = 1,212 vs. 263,426) or higher education entry rates (odds ratio 1.067, 95% CI 0.919-1.239, P < 0.39, n = 965 vs. 217,191), despite nine more sessions of absence from school annually (P < 0.0001). However, attainment in children in the most optimal HbA1c quintile was substantially better than for children without diabetes (0.267 SD, 95% CI 0.160-0.374, P < 0.001) while being worse than for children without diabetes in the least optimal quintile (-0.395 SD, 95% CI -0.504 to -0.287, P < 0.001). Attainment did not differ by duration of "exposure" to diabetes based on age at diagnosis. CONCLUSIONS: Despite more school absences, diabetes diagnosis is not associated with educational attainment or entry into higher education, although attainment does vary by HbA1c level, which may be explained in part (or wholly) by unobserved shared personal, family, or socioeconomic characteristics associated with both success in education and effective glycemic self-management.

Digital solution for detection of undiagnosed diabetes using machine learning-based retinal image analysis.

INTRODUCTION: Undiagnosed diabetes is a global health issue. Previous studies have estimated that about 24.1%-75.1% of all diabetes cases are undiagnosed, leading to more diabetic complications and inducing huge healthcare costs. Many current methods for diabetes diagnosis rely on metabolic indices and are subject to considerable variability. In contrast, a digital approach based on retinal image represents a stable marker of overall glycemic status. RESEARCH DESIGN AND METHODS: Our study involves 2221 subjects for developing a classification model, with 945 subjects with diabetes and 1276 controls. The training data included 70% and the testing data 30% of the subjects. All subjects had their retinal images taken using a non-mydriatic fundus camera. Two separate data sets were used for external validation. The Hong Kong testing data contain 734 controls without diabetes and 660 subjects with diabetes, and the UK testing data have 1682 subjects with diabetes. RESULTS: The 10-fold cross-validation using the support vector machine approach has a sensitivity of 92% and a specificity of 96.2%. The separate testing data from Hong Kong provided a sensitivity of 99.5% and a specificity of 91.1%. For the UK testing data, the sensitivity is 98.0%. The accuracy of the Caucasian retinal images is comparable with that of the Asian data. It implies that the digital method can be applied globally. Those with diabetes complications in both Hong Kong and UK data have a higher probability of risk of diabetes compared with diabetes subjects without complications. CONCLUSIONS: A digital machine learning-based method to estimate the risk of diabetes based on retinal images has been developed and validated using both Asian and Caucasian data. Retinal image analysis is a fast, convenient, and non-invasive technique for community health applications. In addition, it is an ideal solution for undiagnosed diabetes prescreening.

Built Environments And Child Health in WalEs and AuStralia (BEACHES): a study protocol.

INTRODUCTION: Childhood obesity and physical inactivity are two of the most significant modifiable risk factors for the prevention of non-communicable diseases (NCDs). Yet, a third of children in Wales and Australia are overweight or obese, and only 20% of UK and Australian children are sufficiently active. The purpose of the Built Environments And Child Health in WalEs and AuStralia (BEACHES) study is to identify and understand how complex and interacting factors in the built environment influence modifiable risk factors for NCDs across childhood. METHODS AND ANALYSIS: This is an observational study using data from five established cohorts from Wales and Australia: (1) Wales Electronic Cohort for Children; (2) Millennium Cohort Study; (3) PLAY Spaces and Environments for Children's Physical Activity study; (4) The ORIGINS Project; and (5) Growing Up in Australia: the Longitudinal Study of Australian Children. The study will incorporate a comprehensive suite of longitudinal quantitative data (surveys, anthropometry, accelerometry, and Geographic Information Systems data) to understand how the built environment influences children's modifiable risk factors for NCDs (body mass index, physical activity, sedentary behaviour and diet). ETHICS AND DISSEMINATION: This study has received the following approvals: University of Western Australia Human Research Ethics Committee (2020/ET000353), Ramsay Human Research Ethics Committee (under review) and Swansea University Information Governance Review Panel (Project ID: 1001). Findings will be reported to the following: (1) funding bodies, research institutes and hospitals supporting the BEACHES project; (2) parents and children; (3) school management teams; (4) existing and new industry partner networks; (5) federal, state and local governments to inform policy; as well as (6) presented at local, national and international conferences; and (7) disseminated by peer-reviewed publications.

Concussion increases within-player injury risk in male professional rugby union.

OBJECTIVES: To assess within-player change in injury risk and between-player subsequent injury risk associated with concussive and common non-concussive injuries in professional rugby union. METHODS: This prospective cohort study in Welsh professional male rugby union analysed within-player and between-player injury risk for five common injuries: concussion, thigh haematoma, hamstring muscle strain, lateral ankle sprain and acromioclavicular joint sprain. Survival models quantified within-player injury risk by comparing precommon (before) injury risk to postcommon (after) injury risk, whereas between-player subsequent injury risk was quantified by comparing players who had sustained one of the common injuries against those who had not sustained the common injury. HRs and 95% CIs were calculated. Specific body area and tissue type were also determined for new injuries. RESULTS: Concussion increased the within-player overall injury risk (HR 1.26 (95% CI 1.11 to 1.42)), elevating head/neck (HR 1.47 (95% CI 1.18 to 1.83)), pelvic region (HR 2.32 (95% CI 1.18 to 4.54)) and neurological (HR 1.38 (95% CI 1.08 to 1.76)) injury risk. Lateral ankle sprains decreased within-player injury risk (HR 0.77 (95% CI 0.62 to 0.97)), reducing head/neck (HR 0.60 (95% CI 0.39 to 0.91)), upper leg and knee (HR 0.56 (95% CI 0.39 to 0.81)), joint and ligament (HR 0.72 (95% CI 0.52 to 0.99)) and neurological (HR 0.55 (95% CI 0.34 to 0.91)) injury risk. Concussion (HR 1.24 (95% CI 1.10 to 1.40)), thigh haematomas (HR 1.18 (95% CI 1.04 to 1.34)) and hamstring muscle strains (HR 1.14 (95% CI 1.01 to 1.29)) increased between-player subsequent injury risk. CONCLUSION: Elevated within-player injury risk was only evident following concussive injuries, while lateral ankle sprains reduced the risk. Both concussion and ankle injuries altered head/neck and neurological injury risk, but in opposing directions. Understanding why management of ankle sprains might be effective, while current concussion management is not at reducing such risks may help inform concussion return to play protocols.

Ranking sets of morbidities using hypergraph centrality.

Multi-morbidity, the health state of having two or more concurrent chronic conditions, is becoming more common as populations age, but is poorly understood. Identifying and understanding commonly occurring sets of diseases is important to inform clinical decisions to improve patient services and outcomes. Network analysis has been previously used to investigate multi-morbidity, but a classic application only allows for information on binary sets of diseases to contribute to the graph. We propose the use of hypergraphs, which allows for the incorporation of data on people with any number of conditions, and also allows us to obtain a quantitative understanding of the centrality, a measure of how well connected items in the network are to each other, of both single diseases and sets of conditions. Using this framework we illustrate its application with the set of conditions described in the Charlson morbidity index using data extracted from routinely collected population-scale, patient level electronic health records (EHR) for a cohort of adults in Wales, UK. Stroke and diabetes were found to be the most central single conditions. Sets of diseases featuring diabetes; diabetes with Chronic Pulmonary Disease, Renal Disease, Congestive Heart Failure and Cancer were the most central pairs of diseases. We investigated the differences between results obtained from the hypergraph and a classic binary graph and found that the centrality of diseases such as paraplegia, which are connected strongly to a single other disease is exaggerated in binary graphs compared to hypergraphs. The measure of centrality is derived from the weighting metrics calculated for disease sets and further investigation is needed to better understand the effect of the metric used in identifying the clinical significance and ranked centrality of grouped diseases. These initial results indicate that hypergraphs can be used as a valuable tool for analysing previously poorly understood relationships and information available in EHR data.

A retrospective epidemiological study of type 1 diabetes mellitus in wales, UK between 2008 and 2018.

INTRODUCTION: Studies 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. OBJECTIVES: To describe the prevalence, incidence and demographics of persons with type 1 diabetes diagnosed in Wales, UK, using the Secure Anonymised Information Linkage (SAIL) Databank. METHODS: Data 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. RESULTS: The 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. CONCLUSION: This 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.

Characteristics of repeat non-attenders at Diabetes Eye Screening Wales, a national community-based diabetes-related retinopathy screening service, during 2003-2018.

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.

Protocol for the development of the Wales Multimorbidity e-Cohort (WMC): data sources and methods to construct a population-based research platform to investigate multimorbidity.

INTRODUCTION: Multimorbidity is widely recognised as the presence of two or more concurrent long-term conditions, yet remains a poorly understood global issue despite increasing in prevalence.We have created the Wales Multimorbidity e-Cohort (WMC) to provide an accessible research ready data asset to further the understanding of multimorbidity. Our objectives are to create a platform to support research which would help to understand prevalence, trajectories and determinants in multimorbidity, characterise clusters that lead to highest burden on individuals and healthcare services, and evaluate and provide new multimorbidity phenotypes and algorithms to the National Health Service and research communities to support prevention, healthcare planning and the management of individuals with multimorbidity. METHODS AND ANALYSIS: The WMC has been created and derived from multisourced demographic, administrative and electronic health record data relating to the Welsh population in the Secure Anonymised Information Linkage (SAIL) Databank. The WMC consists of 2.9 million people alive and living in Wales on the 1 January 2000 with follow-up until 31 December 2019, Welsh residency break or death. Published comorbidity indices and phenotype code lists will be used to measure and conceptualise multimorbidity.Study outcomes will include: (1) a description of multimorbidity using published data phenotype algorithms/ontologies, (2) investigation of the associations between baseline demographic factors and multimorbidity, (3) identification of temporal trajectories of clusters of conditions and multimorbidity and (4) investigation of multimorbidity clusters with poor outcomes such as mortality and high healthcare service utilisation. ETHICS AND DISSEMINATION: The SAIL Databank independent Information Governance Review Panel has approved this study (SAIL Project: 0911). Study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals.

Risk factors for having diabetic retinopathy at first screening in persons with type 1 diabetes diagnosed under 18 years of age.

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 HbA1c, 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 HbA1c 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.

Understanding and responding to COVID-19 in Wales: protocol for a privacy-protecting data platform for enhanced epidemiology and evaluation of interventions.

INTRODUCTION: The emergence of the novel respiratory SARS-CoV-2 and subsequent COVID-19 pandemic have required rapid assimilation of population-level data to understand and control the spread of infection in the general and vulnerable populations. Rapid analyses are needed to inform policy development and target interventions to at-risk groups to prevent serious health outcomes. We aim to provide an accessible research platform to determine demographic, socioeconomic and clinical risk factors for infection, morbidity and mortality of COVID-19, to measure the impact of COVID-19 on healthcare utilisation and long-term health, and to enable the evaluation of natural experiments of policy interventions. METHODS AND ANALYSIS: Two privacy-protecting population-level cohorts have been created and derived from multisourced demographic and healthcare data. The C20 cohort consists of 3.2 million people in Wales on the 1 January 2020 with follow-up until 31 May 2020. The complete cohort dataset will be updated monthly with some individual datasets available daily. The C16 cohort consists of 3 million people in Wales on the 1 January 2016 with follow-up to 31 December 2019. C16 is designed as a counterfactual cohort to provide contextual comparative population data on disease, health service utilisation and mortality. Study outcomes will: (a) characterise the epidemiology of COVID-19, (b) assess socioeconomic and demographic influences on infection and outcomes, (c) measure the impact of COVID-19 on short -term and longer-term population outcomes and (d) undertake studies on the transmission and spatial spread of infection. ETHICS AND DISSEMINATION: The Secure Anonymised Information Linkage-independent Information Governance Review Panel has approved this study. The study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals.

On average, a professional rugby union player is more likely than not to sustain a concussion after 25 matches.

OBJECTIVES: To 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. METHODS: A 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. RESULTS: Concussion 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. CONCLUSION: Concussion 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.

Playing surface and UK professional rugby union injury risk.

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.