Exploring ethnicity dynamics in Wales: a longitudinal population-scale linked data study and development of a harmonised ethnicity spine.

OBJECTIVE: This study aims to create a national ethnicity spine based on all available ethnicity records in linkable anonymised electronic health record and administrative data sources. DESIGN: A longitudinal study using anonymised individual-level population-scale ethnicity data from 26 data sources available within the Secure Anonymised Information Linkage Databank. SETTING: The national ethnicity spine is created based on longitudinal national data for the population of Wales-UK over 22 years (between 2000 and 2021). PROCEDURE AND PARTICIPANTS: A total of 46 million ethnicity records for 4 297 694 individuals have been extracted, harmonised, deduplicated and made available within a longitudinal research ready data asset. OUTCOME MEASURES: (1) Comparing the distribution of ethnicity records over time for four different selection approaches (latest, mode, weighted mode and composite) across age bands, sex, deprivation quintiles, health board and residential location and (2) distribution and completeness of records against the ONS census 2011. RESULTS: The distribution of the dominant group (white) is minimally affected based on the four different selection approaches. Across all other ethnic group categorisations, the mixed group was most susceptible to variation in distribution depending on the selection approach used and varied from a 0.6% prevalence across the latest and mode approach to a 1.1% prevalence for the weighted mode, compared with the 3.1% prevalence for the composite approach. Substantial alignment was observed with ONS 2011 census with the Latest group method (kappa=0.68, 95% CI (0.67 to 0.71)) across all subgroups. The record completeness rate was over 95% in 2021. CONCLUSION: In conclusion, our development of the population-scale ethnicity spine provides robust ethnicity measures for healthcare research in Wales and a template which can easily be deployed in other trusted research environments in the UK and beyond.

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.

Impact of an injury hospital admission on childhood academic performance: a Welsh population-based data linkage study.

BACKGROUND: While injuries can impact on children's educational achievements (with threats to their development and employment prospects), these risks are poorly quantified. This population-based longitudinal study investigated the impact of an injury-related hospital admission on Welsh children's academic performance. METHODS: The Secure Anonymised Information Linkage databank, 55 587 children residing in Wales from 2006 to 2016 who had an injury hospital admission (58.2% males; 16.8% born in most deprived Wales area; 80.1% one injury hospital admission) were linked to data from the Wales Electronic Cohort for Children. The primary outcome was the Core Subject Indicator reflecting educational achievement at key stages 2 (school years 3-6), 3 (school years 7-9) and 4 (school years 10-11). Covariates in models included demographic, birth, injury and school characteristics. RESULTS: Educational achievement of children was negatively associated with: pedestrian injuries (adjusted risk ratio, (95% CIs)) (0.87, (0.83 to 0.92)), cyclist (0.96, (0.94 to 0.99)), high fall (0.96, (0.94 to 0.97)), fire/flames/smoke (0.85, (0.73 to 0.99)), cutting/piercing object (0.96, (0.93 to 0.99)), intentional self-harm (0.86, (0.82 to 0.91)), minor traumatic brain injury (0.92, (0.86 to 0.99)), contusion/open wound (0.93, (0.91 to 0.95)), fracture of vertebral column (0.78, (0.64 to 0.95)), fracture of femur (0.88, (0.84 to 0.93)), internal abdomen/pelvic haemorrhage (0.82, (0.69 to 0.97)), superficial injury (0.94, (0.92 to 0.97)), young maternal age (<18 years: 0.91, (0.88 to 0.94); 19-24 years: 0.94, (0.93 to 0.96)); area based socioeconomic status (0.98, (0.97 to 0.98)); moving to a more deprived area (0.95, (0.93 to 0.97)); requiring special educational needs (0.46, (0.44 to 0.47)). Positive associations were: being female (1.04, (1.03 to 1.06)); larger pupil school sizes and maternal age 30+ years. CONCLUSION: This study highlights the importance on a child's education of preventing injuries and implementing intervention programmes that support injured children. Greater attention is needed on equity-focused educational support and social policies addressing needs of children at risk of underachievement, including those from families experiencing poverty. VIBES-JUNIOR STUDY PROTOCOL: http://dx.doi.org/10.1136/bmjopen-2018-024755.

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.