How does the environment in and around the home affect social care and health outcomes for older people? A national longitudinal dynamic cohort study.

BACKGROUND: Reducing the burden of falls and fall-related admissions to hospital and care homes is an important policy area because falls cause significant injury leading to a reduced quality of life. We investigated the effect of the environment around people's homes on the risk of falls for older people in Wales. METHODS: In this longitudinal cohort study, we created a dynamic national e-cohort of individuals aged 60 years or older living in Wales between Jan 1, 2010, and Dec 31, 2019. Using the Secure Anonymised Information Linkage Databank, we linked routinely collected, anonymised health-data on general practitioner (GP) appointments; hospital and emergency admissions; and longitudinal individual-level demographic data to metrics detailing the built environment and deprivation as determined by the Welsh Index of Multiple Deprivation. Using adjusted cox regression models, we assessed how the risk of a fall changed with sex, age, deprivation quintile, urban or rural classification, household occupancy, care status, frailty, dementia diagnosis, and built environment metrics. Built environments of urban and rural areas are very different, so we stratified our analysis by urbanicity to compare these associations in each setting. FINDINGS: We analysed 5 536 444 person-years of data from 931 830 individuals (sex: 51·5% female, 48·5% male; age: 69·2% aged 60-64 years, 12·3% aged 65-69 years, 13·3% aged 70-79 years, 4·4% aged 80-89 years, and 0·7% aged ≥90 years). 154 060 (16·5%) had a fall between joining the cohort and Dec 31, 2019. Men had a lower risk of falling than women (adjusted hazard ratio [aHR] 0·736 [0·729-0·742]), and the risk increased with age compared with individuals aged 60-64 years (1·395 [1·378-1·412] for 65-69 years, 1·892 [1·871-1·913] for 70-79 years, 2·668 [2·623-2·713] for 80-89 years, 3·196 [3·063-3·335] for ≥90 years) and with frailty compared with fit individuals (1·609 [1·593-1·624] for mild frailty, 2·263 [2·234-2·293] for moderate frailty, and 2·833 [2·770-2·897] for severe frailty). Those living in rural areas were less likely to fall than those in urban areas (0·711 [0·702-0·720]). All p values were less than 0·0001. INTERPRETATION: Although preliminary, these results corroborate current knowledge that as we age and become frailer, the risk of falling increases. The effect of urbanicity on risk of fall suggests that the built environment could be associated with fall risk. We only detected falls that caused emergency or hospital admission, leading to potential selection bias. Nevertheless, this research could help guide policy to reduce the incidence of injuries caused by falls in older people. FUNDING: Health and Care Research Wales.

Effect of the COVID-19 pandemic on incidence of long-term conditions in Welsh residents: a population linkage study.

BACKGROUND: The COVID-19 pandemic had direct and indirect effects on health. Indirect effects on long term medical conditions (LTCs) are unclear. We examined trends in recorded incidences of LTCs and quantified differences between expected rates and observed rates from 2020 onwards. METHODS: This is a population data linkage study using primary and secondary care data within the Secure Anonymised Information Linkage Databank. We included data of Welsh residents diagnosed with any of 17 identified LTCs for the first time between Jan 1, 2000, and Dec 31, 2021. LTC's include mental health conditions, respiratory diseases, and heart conditions among others, generally chosen in line with the Quality and Outcomes Framework. The primary outcome was incidence rates (monthly number of new cases per 100 000 population). For each LTC, we did interrupted time series analysis of incidence rates from 2015 to 2021. Expected rates from between Jan 1, 2020, to Dec 31, 2021, were predicted using overall trends and seasonal patterns from the preceding 5 years and compared with observed rates. FINDINGS: We included 5 476 012 diagnoses from 2 257 992 individuals diagnosed with at least one LTC between Jan 1, 2000, to Dec 31, 2021. Across multiple long-term conditions, there was an abrupt reduction in observed incidence of new diagnoses from March to April 2020, followed by a general increase in incidence towards prepandemic rates. The conditions with the largest percentage difference between the observed and expected incidence rates in 2020 and 2021 were chronic obstructive pulmonary disease (38·4% lower than expected), depression (28·3% lower), hypertension (25·5% lower), and anxiety disorders (24·9% lower). The condition with the largest absolute difference between observed and expected incidence rates was anxiety disorders, with 830 per 100 000 less in 2020 and 2021 compared with observed rates. INTERPRETATION: The reduction in incidence rates of LTCs suggests an underreporting of LTCs, especially during 2020 and early 2021. The emergence of these yet undiagnosed cases could result in a surge of new patients in the near future. FUNDING: This work was supported by the Wales COVID-19 Evidence Centre, funded by Health and Care Research Wales.

Risk Factors Associated With Primary Care-Reported Domestic Violence for Women Involved in Family Law Care Proceedings: Data Linkage Observational Study.

BACKGROUND: Domestic violence and abuse (DVA) has a detrimental impact on the health and well-being of children and families but is commonly underreported, with an estimated prevalence of 5.5% in England and Wales in 2020. DVA is more common in groups considered vulnerable, including those involved in public law family court proceedings; however, there is a lack of evidence regarding risk factors for DVA among those involved in the family justice system. OBJECTIVE: This study examines risk factors for DVA within a cohort of mothers involved in public law family court proceedings in Wales and a matched general population comparison group. METHODS: We linked family justice data from the Children and Family Court Advisory and Support Service (Cafcass Cymru [Wales]) to demographic and electronic health records within the Secure Anonymised Information Linkage (SAIL) Databank. We constructed 2 study cohorts: mothers involved in public law family court proceedings (2011-2019) and a general population group of mothers not involved in public law family court proceedings, matched on key demographics (age and deprivation). We used published clinical codes to identify mothers with exposure to DVA documented in their primary care records and who therefore reported DVA to their general practitioner. Multiple logistic regression analyses were used to examine risk factors for primary care-recorded DVA. RESULTS: Mothers involved in public law family court proceedings were 8 times more likely to have had exposure to DVA documented in their primary care records than the general population group (adjusted odds ratio [AOR] 8.0, 95% CI 6.6-9.7). Within the cohort of mothers involved in public law family court proceedings, risk factors for DVA with the greatest effect sizes included living in sparsely populated areas (AOR 3.9, 95% CI 2.8-5.5), assault-related emergency department attendances (AOR 2.2, 95% CI 1.5-3.1), and mental health conditions (AOR 1.7, 95% CI 1.3-2.2). An 8-fold increased risk of DVA emphasizes increased vulnerabilities for individuals involved in public law family court proceedings. CONCLUSIONS: Previously reported DVA risk factors do not necessarily apply to this group of women. The additional risk factors identified in this study could be considered for inclusion in national guidelines. The evidence that living in sparsely populated areas and assault-related emergency department attendances are associated with increased risk of DVA could be used to inform policy and practice interventions targeting prevention as well as tailored support services for those with exposure to DVA. However, further work should also explore other sources of DVA, such as that recorded in secondary health care, family, and criminal justice records, to understand the true scale of the problem.

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.

Risk factors associated with unintentional house fire incidents, injuries and deaths in high-income countries: a systematic review.

OBJECTIVE: To identify the distinguishing risk factors associated with unintentional house fire incidents, injuries and deaths. STUDY DESIGN: Systematic review. METHODS: A range of bibliographical databases and grey literature were searched from their earliest records to January 2016. To ensure the magnitude of risk could be quantified, only those study types which contained a control group, and undertook appropriate statistical analyses were included. A best evidence synthesis was conducted instead of a meta-analysis due to study heterogeneity. RESULTS: Eleven studies investigating a variety of risk factors and outcomes were identified. Studies ranged from medium to low quality with no high quality studies identified. Characteristics commonly associated with increased risk of house fire incidents, injuries and fatalities included: higher numbers of residents, male, children under the age of 5 years, non-working households, smoking, low income, non-privately owned properties, apartments and buildings in poor condition. Several risk factors were only associated with one outcome (eg, living alone was only associated with increased risk of injurious fires), and households with older residents were at increased risk of injurious fires, but significantly less likely to experience a house fire in the first place. CONCLUSIONS: This best evidence synthesis indicates that several resident and property characteristics are associated with risk of experiencing house fire incidents, injuries or death. These findings should be considered by the Fire and Rescue Services and others with a role in fire prevention. Future research should adopt robust, standardised study designs to permit meta-analyses and enable stronger conclusions to be drawn.

Cardiac interventions in Wales: A comparison of benefits between NHS Wales specialties.

OBJECTIVES: The study aimed to assess if specialised healthcare service interventions in Wales benefit the population equitably in work commissioned by the Welsh Health Specialised Services Committee (WHSSC). APPROACH: The study utilised anonymised individual-level, population-scale, routinely collected electronic health record (EHR) data held in the Secure Anonymised Information Linkage (SAIL) Databank to identify patients resident in Wales receiving specialist cardiac interventions. Measurement was undertaken of associated patient outcomes 2-years before and after the intervention (minus a 6-month clearance period on either side) by measuring events in primary care, hospital attendance, outpatient and emergency department. The analysis controlled for comorbidity (Charlson) and deprivation (Welsh Index of Multiple Deprivation), stratified by admission type (elective or emergency) and membership of top 5% post-intervention costs. Costs were estimated by multiplying events by mean person cost estimates. RESULTS: We identified 5,999 percutaneous coronary interventions (PCI) and 1,640 coronary artery bypass graft (CABG) between 2014-06-01 to 2020-02-29. The ratio of emergency to elective interventions was 2.85 for PCI and 1.04 for CABG. In multivariate analysis significant associations were identified for comorbidity (OR = 1.52, CI = (1.01-2.27)), deprivation (OR = 1.34, CI = (1.03-1.76)) and rurality (OR = 0.81, CI = (0.70-0.95)) for PCI interventions, and comorbidity (OR = 1.47, CI = (1.10-1.98)) for CABG. Higher costs post-intervention were associated with increased comorbidity for PCI and CABG in the top 5% cost groups, but for PCI this was not seen outside the top 5%. For PCI, moderate cost increase was associated with increased deprivation, but the picture was more mixed following CABG interventions. For both interventions, lower costs post intervention were seen in rural locations. CONCLUSION: We identified and compared health outcomes for selected specialist cardiac interventions amongst patients resident in Wales, with these methods and analyses, providing a template for comparing other cardiac interventions.

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.

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.

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.

Patterns of Healthcare Resource Utilisation of Critical Care Survivors between 2006 and 2017 in Wales: A Population-Based Study.

In this retrospective cohort study, we used the Secure Anonymised Information Linkage (SAIL) Databank to characterise and identify predictors of the one-year post-discharge healthcare resource utilisation (HRU) of adults who were admitted to critical care units in Wales between 1 April 2006 and 31 December 2017. We modelled one-year post-critical-care HRU using negative binomial models and used linear models for the difference from one-year pre-critical-care HRU. We estimated the association between critical illness and post-hospitalisation HRU using multilevel negative binomial models among people hospitalised in 2015. We studied 55,151 patients. Post-critical-care HRU was 11-87% greater than pre-critical-care levels, whereas emergency department (ED) attendances decreased by 30%. Age ≥50 years was generally associated with greater post-critical-care HRU; those over 80 had three times longer hospital readmissions than those younger than 50 (incidence rate ratio (IRR): 2.96, 95% CI: 2.84, 3.09). However, ED attendances were higher in those younger than 50. High comorbidity was associated with 22-62% greater post-critical-care HRU than no or low comorbidity. The most socioeconomically deprived quintile was associated with 24% more ED attendances (IRR: 1.24 [1.16, 1.32]) and 13% longer hospital stays (IRR: 1.13 [1.09, 1.17]) than the least deprived quintile. Critical care survivors had greater 1-year post-discharge HRU than non-critical inpatients, including 68% longer hospital stays (IRR: 1.68 [1.63, 1.74]). Critical care survivors, particularly those with older ages, high comorbidity, and socioeconomic deprivation, used significantly more primary and secondary care resources after discharge compared with their baseline and non-critical inpatients. Interventions are needed to ensure that key subgroups are identified and adequately supported.

Real-world effectiveness of molnupiravir, nirmatrelvir-ritonavir, and sotrovimab on preventing hospital admission among higher-risk patients with COVID-19 in Wales: A retrospective cohort study.

OBJECTIVE: To compare the effectiveness of molnupiravir, nirmatrelvir-ritonavir, and sotrovimab with no treatment in preventing hospital admission or death in higher-risk patients infected with SARS-CoV-2 in the community. DESIGN: Retrospective cohort study of non-hospitalized adult patients with COVID-19 using the Secure Anonymised Information Linkage (SAIL) Databank. SETTING: A real-world cohort study was conducted within the SAIL Databank (a secure trusted research environment containing anonymised, individual, population-scale electronic health record (EHR) data) for the population of Wales, UK. PARTICIPANTS: Adult patients with COVID-19 in the community, at higher risk of hospitalization and death, testing positive for SARS-CoV-2 between 16th December 2021 and 22nd April 2022. INTERVENTIONS: Molnupiravir, nirmatrelvir-ritonavir, and sotrovimab given in the community by local health boards and the National Antiviral Service in Wales. MAIN OUTCOME MEASURES: All-cause admission to hospital or death within 28 days of a positive test for SARS-CoV-2. STATISTICAL ANALYSIS: Cox proportional hazard model with treatment status (treated/untreated) as a time-dependent covariate and adjusted for age, sex, number of comorbidities, Welsh Index of Multiple Deprivation, and vaccination status. Secondary subgroup analyses were by treatment type, number of comorbidities, and before and on or after 20th February 2022, when omicron BA.1 and omicron BA.2 were the dominant subvariants in Wales. RESULTS: Between 16th December 2021 and 22nd April 2022, 7013 higher-risk patients were eligible for inclusion in the study. Of these, 2040 received treatment with molnupiravir (359, 17.6%), nirmatrelvir-ritonavir (602, 29.5%), or sotrovimab (1079, 52.9%). Patients in the treatment group were younger (mean age 53 vs 57 years), had fewer comorbidities, and a higher proportion had received four or more doses of the COVID-19 vaccine (36.3% vs 17.6%). Within 28 days of a positive test, 628 (9.0%) patients were admitted to hospital or died (84 treated and 544 untreated). The primary analysis indicated a lower risk of hospitalization or death at any point within 28 days in treated participants compared to those not receiving treatment. The adjusted hazard rate was 35% (95% CI: 18-49%) lower in treated than untreated participants. There was no indication of the superiority of one treatment over another and no evidence of a reduction in risk of hospitalization or death within 28 days for patients with no or only one comorbidity. In patients treated with sotrovimab, the event rates before and on or after 20th February 2022 were similar (5.0% vs 4.9%) with no significant difference in the hazard ratios for sotrovimab between the time periods. CONCLUSIONS: In higher-risk adult patients in the community with COVID-19, those who received treatment with molnupiravir, nirmatrelvir-ritonavir, or sotrovimab were at lower risk of hospitalization or death than those not receiving treatment.

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.

Impact of COVID-19 pandemic on incidence of long-term conditions in Wales: a population data linkage study using primary and secondary care health records.

BACKGROUND: The COVID-19 pandemic has directly and indirectly had an impact on health service provision owing to surges and sustained pressures on the system. The effects of these pressures on the management of long-term or chronic conditions are not fully understood. AIM: To explore the effects of COVID-19 on the recorded incidence of 17 long-term conditions. DESIGN AND SETTING: This was an observational retrospective population data linkage study on the population of Wales using primary and secondary care data within the Secure Anonymised Information Linkage (SAIL) Databank. METHOD: Monthly rates of new diagnosis between 2000 and 2021 are presented for each long-term condition. Incidence rates post-2020 were compared with expected rates predicted using time series modelling of pre-2020 trends. The proportion of annual incidence is presented by sociodemographic factors: age, sex, social deprivation, ethnicity, frailty, and learning disability. RESULTS: A total of 5 476 012 diagnoses from 2 257 992 individuals are included. Incidence rates from 2020 to 2021 were lower than mean expected rates across all conditions. The largest relative deficit in incidence was in chronic obstructive pulmonary disease corresponding to 343 (95% confidence interval = 230 to 456) undiagnosed patients per 100 000 population, followed by depression, type 2 diabetes, hypertension, anxiety disorders, and asthma. A GP practice of 10 000 patients might have over 400 undiagnosed long-term conditions. No notable differences between sociodemographic profiles of post- and pre-2020 incidences were observed. CONCLUSION: There is a potential backlog of undiagnosed patients with multiple long-term conditions. Resources are required to tackle anticipated workload as part of COVID-19 recovery, particularly in primary care.

Dimensions of equality in uptake of COVID-19 vaccination in Wales, UK: A multivariable linked data population analysis.

Vaccination has proven to be effective at preventing severe outcomes of COVID-19 infection, and uptake in the population has been high in Wales. However, there is a risk that high-level vaccination coverage statistics may mask hidden inequalities in under-served populations, many of whom may be at increased risk of severe outcomes of COVID-19 infection. The study population included 1,436,229 individuals aged 18 years and over, alive and residence in Wales as at 31st July 2022, and excluded immunosuppressed or care home residents. We compared people who had received one or more vaccinations to those with no vaccination using linked data from nine datasets within the Secure Anonymised Information Linkage (SAIL) databank. Multivariable analysis was undertaken to determine the impact of a range of sociodemographic characteristics on vaccination uptake, including ethnicity, country of birth, severe mental illness, homelessness and substance use. We found that overall uptake of first dose of COVID-19 vaccination was high in Wales (92.1 %), with the highest among those aged 80 years and over and females. Those aged under 40 years, household composition (aOR 0.38 95 %CI 0.35-0.41 for 10+ size household compared to two adult household) and being born outside the UK (aOR 0.44 95 %CI 0.43-0.46) had the strongest negative associations with vaccination uptake. This was followed by a history of substance misuse (aOR 0.45 95 %CI 0.44-0.46). Despite high-level population coverage in Wales, significant inequalities remain across several underserved groups. Factors associated with vaccination uptake should not be considered in isolation, to avoid drawing incorrect conclusions. Ensuring equitable access to vaccination is essential to protecting under-served groups from COVID-19 and further work needs to be done to address these gaps in coverage, with focus on tailored vaccination pathways and advocacy, using trusted partners and communities.

Equity of access to NHS-funded hip replacements in England and Wales: Trends from 2006 to 2016.

Background: Elective hip replacement is a cost-effective means of improving hip function. Previous research has suggested that the supply of hip replacements in the NHS is governed by the inverse care law. We examine whether inequities in supply improved in England and Wales between 2006 and 2016. Methods: We compare levels of need and supply of NHS funded hip replacements to adults aged 50+ years, across quintiles of deprivation in England and Wales between 2006 and 2016. We use data from routine health records and a large longitudinal study and adjust for age and sex using general additive negative-binomial regression. Findings: The number of NHS-funded hip replacements per 100,000 population rose substantially from 272.6 and 266.7 in 2002, to 539.7 and 466.3 in 2018 in England and Wales respectively. Having adjusted for age and sex, people living in the most deprived quintile were 2.36 (95% CI, 1.69 to 3.29) times more likely to need a hip replacement in 2006 than those living in quintile 3, whereas those living in the least deprived quintile were 0.45 (95% CI, 0.39 to 0.69) as likely. Despite this, people living in the most deprived quintile were 0.81 (95% CI, 0.78 to 0.83) times as likely in England and 0.93 (95% CI, 0.84 to 1.04) as likely in Wales to receive an NHS-funded hip replacement in 2006 than those living in quintile 3. We found no evidence that these substantial inequities had reduced between 2006 and 2016. Interpretation: With respect to hip-replacement surgery in England and Wales, policy ambitions to reduce healthcare inequities have not been realised. Funding: This work was supported by Health Data Research UK.

A methodology to facilitate critical care research using multiple linked electronic, clinical and administrative health records at population scale.

Introduction: Critical Care is a specialty in medicine providing a service for severely ill and high-risk patients who, due to the nature of their condition, may require long periods recovering after discharge. Consequently, focus on the routine data collection carried out in Intensive Care Units (ICUs) leads to reporting that is confined to the critical care episode and is typically insensitive to variation in individual patient pathways through critical care to recovery.A resource which facilitates efficient research into interactions with healthcare services surrounding critical admissions, capturing the complete patient's healthcare trajectory from primary care to non-acute hospital care prior to ICU, would provide an important longer-term perspective for critical care research. Objective: To describe and apply a reproducible methodology that demonstrates how both routine administrative and clinically rich critical care data sources can be integrated with primary and secondary healthcare data to create a single dataset that captures a broader view of patient care. Method: To demonstrate the INTEGRATE methodology, it was applied to routine administrative and clinical healthcare data sources in the Secure Anonymised Data Linking (SAIL) Databank to create a dataset of patients' complete healthcare trajectory prior to critical care admission. SAIL is a national, data safe haven of anonymised linkable datasets about the population of Wales. Results: When applying the INTEGRATE methodology in SAIL, between 2010 and 2019 we observed 91,582 critical admissions for 76,019 patients. Of these, 90,632 (99%) had an associated non-acute hospital admission, 48,979 (53%) had an emergency admission, and 64,832 (71%) a primary care interaction in the week prior to the critical care admission. Conclusion: This methodology, at population scale, integrates two critical care data sources into a single dataset together with data sources on healthcare prior to critical admission, thus providing a key research asset to study critical care pathways.

Evaluation of the shielding initiative in Wales (EVITE Immunity): protocol for a quasiexperimental study.

INTRODUCTION: Shielding aimed to protect those predicted to be at highest risk from COVID-19 and was uniquely implemented in the UK during the COVID-19 pandemic. Clinically extremely vulnerable people identified through algorithms and screening of routine National Health Service (NHS) data were individually and strongly advised to stay at home and strictly self-isolate even from others in their household. This study will generate a logic model of the intervention and evaluate the effects and costs of shielding to inform policy development and delivery during future pandemics. METHODS AND ANALYSIS: This is a quasiexperimental study undertaken in Wales where records for people who were identified for shielding were already anonymously linked into integrated data systems for public health decision-making. We will: interview policy-makers to understand rationale for shielding advice to inform analysis and interpretation of results; use anonymised individual-level data to select people identified for shielding advice in March 2020 and a matched cohort, from routine electronic health data sources, to compare outcomes; survey a stratified random sample of each group about activities and quality of life at 12 months; use routine and newly collected blood data to assess immunity; interview people who were identified for shielding and their carers and NHS staff who delivered healthcare during shielding, to explore compliance and experiences; collect healthcare resource use data to calculate implementation costs and cost-consequences. Our team includes people who were shielding, who used their experience to help design and deliver this study. ETHICS AND DISSEMINATION: The study has received approval from the Newcastle North Tyneside 2 Research Ethics Committee (IRAS 295050). We will disseminate results directly to UK government policy-makers, publish in peer-reviewed journals, present at scientific and policy conferences and share accessible summaries of results online and through public and patient networks.

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.

Frailty assessed by administrative tools and mortality in patients with pneumonia admitted to the hospital and ICU in Wales.

The ideal method of identifying frailty is uncertain, and data on long-term outcomes is relatively limited. We examined frailty indices derived from population-scale linked data on Intensive Care Unit (ICU) and hospitalised non-ICU patients with pneumonia to elucidate the influence of frailty on mortality. Longitudinal cohort study between 2010-2018 using population-scale anonymised data linkage of healthcare records for adults admitted to hospital with pneumonia in Wales. Primary outcome was in-patient mortality. Odds Ratios (ORs [95% confidence interval]) for age, hospital frailty risk score (HFRS), electronic frailty index (eFI), Charlson comorbidity index (CCI), and social deprivation index were estimated using multivariate logistic regression models. The area under the receiver operating characteristic curve (AUC) was estimated to determine the best fitting models. Of the 107,188 patients, mean (SD) age was 72.6 (16.6) years, 50% were men. The models adjusted for the two frailty indices and the comorbidity index had an increased odds of in-patient mortality for individuals with an ICU admission (ORs for ICU admission in the eFI model 2.67 [2.55, 2.79], HFRS model 2.30 [2.20, 2.41], CCI model 2.62 [2.51, 2.75]). Models indicated advancing age, increased frailty and comorbidity were also associated with an increased odds of in-patient mortality (eFI, baseline fit, ORs: mild 1.09 [1.04, 1.13], moderate 1.13 [1.08, 1.18], severe 1.17 [1.10, 1.23]. HFRS, baseline low, ORs: intermediate 2.65 [2.55, 2.75], high 3.31 [3.17, 3.45]). CCI, baseline < 1, ORs: '1-10' 1.15 [1.11, 1.20], > 10 2.50 [2.41, 2.60]). For predicting inpatient deaths, the CCI and HFRS based models were similar, however for longer term outcomes the CCI based model was superior. Frailty and comorbidity are significant risk factors for patients admitted to hospital with pneumonia. Frailty and comorbidity scores based on administrative data have only moderate ability to predict outcome.

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.