Suchergebnisse

BackgroundThe COVID-19 pandemic caused significant disruption to healthcare services and changed patterns of healthcare resource utilisation in Wales, but the geographic variation of this disruption is not known.
Objective/Approach We aimed to examine geo-spatial variation in the impact of the pandemic on healthcare service use, and identify socio-demographic and environmental factors associated with the geographical differences.
We accessed and linked individual electronic healthcare records within the Secure Anonymised Information Linkage (SAIL) Databank.
We calculated age-sex standardised rates of interactions across 1909 small areas in Wales, from January 2017 to December 2022. We compared rates in the 3 years pre-COVID (2017-2019) to the following 3 years (2020-2022) and investigated if spatial autocorrelation of results existed, using the Moran's I test.
We linked area-level characteristics and carried out Geographically Weighted Regression (GWR) analysis to understand the socio-demographic and environmental characteristics associated with 'hot' and 'cold' spots of healthcare resource use.
Results We included 1,277,532 individuals with 7,502,485 healthcare service interactions related to a long-term condition.
Across the 1909 small areas in Wales, we observed large variations in age-sex standardised rates of healthcare interactions. Area-level general practice and hospital admission rates decreased by a median of 22%, and 13.1% respectively, from 2017-2019 to 2020-2022. The change in general practice rates were more significantly spatially autocorrelated than the change in hospital admission rates.
Spatially varying relationships between healthcare resource use and area-level characteristics such as income deprivation, travel time to general practice/hospital, have been analysed using GWR (results to be finalised).
Conclusions/Implications We have found that the COVID-19 pandemic impacted population level healthcare resource use in some areas of Wales more than others. Identifying the underlying characteristics causing these differences will help inform policy to provide targeted services to areas and subgroups of the population.

BackgroundCardiac arrest occurs when the heart ceases pumping blood around the body. Most take place outside hospital. A sequence of effective interventions (a "chain of survival") is essential for patient survival and recovery of organ function. Out of Hospital Cardiac Arrest (OHCA) requires prompt recognition and call for help, proficient CPR, early defibrillation and conveyance by the emergency services to an appropriate hospital. If initial resuscitation is successful, effective post-resuscitation care is required, delivered usually in the intensive care unit (ICU). New patient pathways were introduced following an update to national guidelines, which recommend conveyance of patients to centres with percutaneous coronary intervention (PCI) services where possible.
ObjectiveWe have evaluated outcomes for patients, comparing those conveyed to PCI settings and those conveyed elsewhere to quantify the effect of the new patient pathway on mortality, survival and length of hospital stay.
ApproachWe have linked electronic health records from emergency departments, acute hospital settings, ICUs and the national deaths register for OHCA patients living in Wales, UK. We have undertaken temporal analysis and time to event analysis to compare patient subgroups.
ResultsVariation in patient pathways will be described using characteristics of conveyance method, the type of treating hospital and transfers between PCI and non-PCI settings adjusted for individual patient level confounders such as age, sex and comorbidities.
ConclusionThis work is important to evaluate efficacy of operational procedures and inform national policies which aim to improve survival following OHCA and save lives.

ObjectivesPhysical inactivity directly contributes to the global issue of obesity. Small-scale garden-based interventions have positively impacted on children's physical activity levels. However, no studies have objectively measured household gardens and assessed associations with childhood obesity on a population scale. We linked garden measures with administrative data for a national population.
ApproachOur cross-sectional study examined the relationship between garden size and BMI in 154,444 children aged 5 years living in Wales between 2013-2019. We linked garden size at the home location with individual-level BMI measurements and socio-demographic data in the SAIL databank; a secure research environment. BMI measures were derived from the Child Measurement Programme and were standardised using the LMS method. We calculated descriptive statistics for our cohort and used Generalised Additive Models to investigate associations between garden size and BMI. Our results include adjustment for confounding variables such as deprivation. We stratified our analysis by gender and rurality.
ResultsOur cohort consisted of 52% male and 49% females. 18% lived in the most affluent quintile and 28% lived in the most deprived quintile. 74% of the cohort lived in urban areas whilst 26% lived in rural areas. Average garden size was 176m2 and the mean number of individuals living in the home was 4. We found a non-linear statistically significant negative association between garden size and BMI (edf = 2.37, p < 0.01). When we stratified our analysis by gender, for males we found a linear statistically significant negative association between garden size and BMI (edf = 1, p < 0.01). For females we found a non-linear statistically significant association between garden size and BMI (edf = 2.67, p < 0.05).
ConclusionOur study is the first to link routinely measured objective household variables with BMI data for a national cohort. Our results suggest that garden-based interventions could be focussed on to increase physical activity in children. Further research is required to investigate the pathways between garden, physical activity and reducing obesity.

IntroductionMore patients are surviving critical illness, with a growing understanding that transition to survivorship needs to be managed in primary care. In Wales, however, survivors' comprehensive healthcare resource utilisation (HRU) has not been studied in detail.
AimTo investigate the levels and predictors of HRU of critical illness survivors in Wales.
MethodsWe used the SAIL Databank to identify survivors and characterise their HRU. We fitted Poisson models for HRU during one year after intensive care unit (ICU) stay, adjusted for follow-up length within that year, HRU in the previous year, modified Charlson Comorbidity Index (CCI), age group, and gender.
ResultsWe identified 56,722 patients. The median age was 66 years (IQR=52-76), 46.3% were female, and 39.0% had high comorbidity (CCI≥10). More patients lived in the more deprived areas. Patients had medians of 29 (0-51) visits to general practitioners (GP), 5 (2-10) outpatient visits, 17 (5-63) days for length of hospital stay (LOS), and a mean of 0.89 emergency department (ED) attendances. Older patients had more GP visits (incidence rate ratio, IRRs, ranged from 1.1 to 1.2) and outpatient visits (1.1), greater LOS (1.2 to 1.7), but fewer ED visits (0.72 to 0.65). Higher CCI was associated with greater HRU (GP [1.19, 1.20], ED [1.34, 1.42], outpatient [1.50, 1.52], LOS [1.24, 1.6]). Higher socioeconomic deprivation was associated with more ED visits (IRR=1.2 [1.17, 1.25] between the most and the least deprived areas).
ConclusionCritical care survivors use significant primary healthcare resources following their ICU stay. Predictors of long-term mortality [1] also influence HRU. The fewer ED visits in the older patients needs further evaluation.
1. Szakmany T et al. Crit Care Med. 2019 Jan;47(1):15–22.

ObjectivesMental wellbeing can deteriorate throughout adolescence; females and children from low-income families more likely to experience mental health conditions. Views of greenspace from home positively impact cognition, but links with wellbeing has not been explored in children. We linked environment and survey data for 14 year olds in Wales, UK.
ApproachOur cross-sectional study examined the relationship between views of greenspace and wellbeing for >1000 children aged 14 years living in Wales between 2015-2016. We linked data on views of greenspace from the home location with individual-level wellbeing and socio-demographic data in the SAIL Databank; a secure research environment. Our health outcome was derived from self-reported wellbeing measures in the Millennium Cohort Study. Views of greenspace were derived from LiDAR data and quantified on a continuous scale (0-1). We used Generalised Additive Models to investigate associations between views of greenspace and wellbeing; adjusting for factors such as parent wellbeing and deprivation.
ResultsHomes in coastal areas had larger views of greenspace than non-coastal residences. Individuals living in the most deprived areas had smaller views of greenspace (mean = 0.03) than least deprived (mean = 0.12). Overall, individuals living in detached homes had the greatest views of greenspace (0.4) and flats had the poorest views of greenspace (mean = 0.02). We will report our final regression analyses at the conference investigating the association between views of greenspace and adolescent wellbeing. Our models will be fully adjusted and sub-analyses will be stratified by gender and urban/rural status. We will also report findings on whether deprivation mediates for any relationships.
ConclusionOur study is the first to link objectively measured views of greenspace with wellbeing data for a national cohort. Our results can be used to develop interventions to support good wellbeing in adolescents. Further longitudinal research is required to investigate the causal pathways between views of greenspace and adolescent wellbeing.

BackgroundAustralia possesses valuable population health data, yet its potential for advancing medical product development remains largely untapped. Transformative growth in the use of linked data in clinical trials will support Australia's therapeutic development sector to be more responsive in the development of new therapeutics and the monitoring and surveillance of their safety and effectiveness.
MethodsWe conducted interviews with clinical trialists and data linkage experts to understand the current state and awareness of linked data within the clinical trials sector, identify existing pain points in the linkage process, and explore opportunities for improving visibility and access for the clinical trials sector. Researchers represented all stages of the medical product development pipeline and had a diverse range of experience with linked data.
ResultsInterviews revealed numerous barriers hindering the clinical trials sector from effectively using linked data, limiting its impact. Barriers included a lack of awareness, the complexity of the application and approval process, and lengthy delays in accessing data.
Participants readily recognised several ways that linkage of real-world data could be applied to their work in clinical trials, with pre-recruitment data, measurements of primary and secondary endpoints, and health economic analysis being the most consistently noted use cases.
ConclusionTo optimise the use of linked data to support clinical trials and medical product development in Australia, increased awareness among the therapeutic development sector regarding available real-world data and its potential is needed urgently. Lowering the barriers to accessing linked data will enhance health services and patient outcomes across Australia.

ABSTRACT
ObjectivesA social housing regeneration programme, delivered by one Welsh local authority, has invested £204M (US$308M) to upgrade its housing stock in order to meet a national housing quality standard; the Welsh Housing Quality Standard (WHQS). Homes may receive new bathrooms, kitchens, heating, windows and doors, and insulation. Our objective is to evaluate the effect of implementing the concentrated WHQS investment policy on the physical and mental health of the residents compared to the standard 'business as usual' policy of housing stock maintenance.
ApproachUsing housing and regeneration data from the Local Authority and an anonymised databank (Secure Anonymised Information Linkage, SAIL) we have established a residence-based cohort. The primary outcome is a relative change in emergency hospital admissions (injuries, cardiovascular and respiratory conditions) following housing improvements; secondary outcomes include health care utilisation costs, primary care mental health conditions and deaths. Different comparator groups will be used to adjust for trends over time.
ResultsWe have linked housing data to individuals and their health data using a novel data linkage system. There are about 32 000 residents in the intervention group, 49 000 in a social housing comparator group; and 231 000 in a large general comparator group. Residents who migrate within the social housing cohort are retained using data linkage methods. Records relating to respiratory and cardiovascular conditions as well as falls and burns were obtained retrospectively for each individual, from 2005 to 2015, with measurements captured in 123 monthly time intervals. We are using a three-level negative binomial model, in which time is nested within individuals within properties, to examine the effects of the housing improvements on the mean number of emergency hospital admissions, whilst adjusting for individual-level explanatory variables such as gender, age and comorbidities.
Conclusion We discuss the challenges and benefits of linking routine housing and health data to evaluate a natural experiment. We have identified differences between residents in the study area and the comparator groups to evaluate the health benefits of a targeted housing improvement program to implement the WHQS.
The evaluation was funded by the National Institute for Health Research, Public Health Research Programme, United Kingdom. (http://www.nets.nihr.ac.uk/projects/phr/09300602).

ObjectivesThe Welsh Health Specialised Services Committee (WHSSC) has commissioned a study to assess if there is a standard value-based approach to measure and evaluate the effectiveness of specific interventions, and whether variation exists that may indicate differences in the equity of access to these services provided to patients in Wales.
ApproachUsing anonymised individual-level, population-scale, routinely collected electronic health record (EHR) data held within the Secure Anonymised Information Linkage (SAIL) Databank, we planned to identify amongst the population of Wales any patients experiencing percutaneous coronary intervention (PCI) and Transcatheter Aortic Valve Implantation (TAVI). We would measure associated patient outcomes 2-years before and after the intervention minus a 6-month clearance period on either side by measuring primary care attendances in general practice, and secondary care attendances in hospital, outpatient and emergency department data. To further inform the analysis, linkage to socio-demographic factors, comorbidities and lifestyle factors would be controlled for.
ResultsPreliminary results identified 5,999 PCIs between June 2014 and March 2020. We were able to identify 1,530 as elective and 4,358 as emergency secondary care procedures. Of the elective PCIs, 184 patients had a greater number of elective hospital days pre-PCI, and 382 patients had a greater number of elective hospital days post-PCI. Of the emergency PCIs, 135 patients had a greater number of emergency hospital days pre-PCI, and 893 patients had a greater number of emergency hospital days post-PCI. For TAVI intervention, we identified 74 elective and 49 emergency procedures. 35 of the elective TAVI had greater hospital days pre-intervention, and 17 of the elective TAVI had greater hospital days post-intervention. Primary care costs slightly rose for PCI and slightly reduced for TAVI.
ConclusionWe successfully identified interventions and measured and contrasted outcomes before and after the intervention using the SAIL Databank. For PCI, the elective intervention showed a reduced increase in bed days compared to emergency intervention. These initial findings allow us to plan an expanded analysis to examine other intervention types.

IntroductionCritical 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.
ObjectiveTo 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.
MethodTo 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.
ResultsWhen 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%) hadan emergency admission, and 64,832 (71%) a primary care interaction in the week prior to the critical care admission.
ConclusionThis 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.

Objective and ApproachThe Welsh Health Specialised Services Committee wanted to demonstrate how specialised interventions reduced subsequent pressures on the health services.
We analysed the variation in the cost of patient pathways for eight cardiac interventions in both planned and emergency settings. A standard cost was allocated to each health event (hospital admissions, general practitioner interactions, outpatient attendances and accident and emergency attendances). A comparison was made for each intervention relating to the condition before the intervention against costs afterwards. In addition, analysis was carried out measuring which patient's characteristics affected the likelihood that patients incurred higher post-intervention costs.
Results10,480 interventions were analysed. In 9 (of the 16) intervention combinations, post-intervention costs dropped. After the implantation of a Cardiac Device, health resource usage dropped by £1,461 per annum. In addition, the likelihood of falling into a higher cost category increased depending on deprivation. For percutaneous coronary intervention (PCI) the odds ratio and 95% confidence interval was 1.34 (1.03 to 1.76) and for Cardiac Devices 2.91 (1 to 15.23).
Conclusions and ImplicationsSome interventions in Cardiac realise significant post intervention savings for the health service. Deprivation affects the risk of patients falling into high post-intervention cost categories for PCI and Cardiac Devices.
By using anonymised population-scale linked individual-level data, it is possible to highlight interventions that as well as benefiting patients successfully, reduce future health resource usage. This type of analysis can also highlight how deprivation is linked to increased post intervention costs.

Objective and ApproachWe analysed the effect on subsequent costs of the setting of the first contact for patients with coronary heart disease in Wales. By categorising health events as proactive or reactive, from the event data source (proactive = primary care (general practice) interactions, elective hospital bed days and outpatient attendances. Reactive = accident and emergency (A&E) attendances and emergency hospital bed days). A standard cost was applied to obtain an annualised cost for each patient. The unit cost for each source was GP £36, bed days £398, outpatient £143, A&E £188.
ResultsWe found 42,563 people having CHD events during 2015-2019. Of these, 18,390 had a proactive event as their first event, 23,840 had a reactive event first. The ratio of proactive to reactive events was 1: 3.7 (ratio of costs were £1: £6.30). When the first event was proactive the ratio of events was 1: 0.85 (costs were £1: £1.33). When the first event was reactive the ratio was 1: 4.4 (costs were £1: £33.00).
Conclusion and ImplicationsThe setting of the first contact appears to be key, with costs reducing by a factor of 14 if the first contact was carried out in a planned environment. Can investment in more active management of a condition reduce overall costs?
The work has highlighted the power of using anonymised population-scale linked individual-level data and indicates the health resource reductions possible from improved managed care of patients.

ObjectivesYouth obesity has increased substantially in recent decades; however, the potential role of the built environment in mitigating these trends is unclear. This study examines whether more walkable neighbourhoods are associated with lower levels of overweight/obesity for adolescents compared to less walkable neighbourhoods, after considering the potential role of socio-economic and lifestyle characteristics.
MethodsWe examine overweight/obesity levels for all singleton 14 years-old children living in Wales, using the UK Millennium Cohort Study. Children are classified as healthy weight, overweight and obese using international age and sex adjusted cut-offs for body mass index (BMI). The built environment is assessed using the active living environments (ALE) index for 2017-2018 classified into 5 categories (1-low walkability and 5 – high walkability). We apply regression analysis and adjust for children characteristics (e.g., physical activity), parental socio-economic circumstances and lifestyle choices (e.g., maternal education, physical activity).
ResultsWe assess the hypothesis that the built environment is associated with adolescents' overweight/obesity levels and examine how much of this association could be modified by parental socioeconomic circumstances and lifestyle choices. The ALE index is higher in urban compared to rural areas. To capture variations in Wales' population, we are conducting a stratified analysis to explore any differences on the association between ALE index and adolescents' overweight/obesity by urban and rural areas. Accounting for the potential role of lifestyle and socio-economic characteristics is key for future research, as understanding the underlying pathways of this association is necessary to design effective interventions.
ConclusionFindings can help us develop a better understanding of associations between the built environment and overweight/obesity status to inform evidence-based planning policy and practice strategies on how to modify the built environment to promote child health in future generations by increasing better opportunities for diet and activity.

ObjectivesIn Wales almost a quarter of adults and 1 in 8 reception age children are obese. Linked data is a key tool to understanding the role of the built environment on obesity rates and is an important part of developing strategies to combat the obesity epidemic in Wales.
ApproachWe set out to develop an analytical platform for generating evidence on key aspects of the built environment which impact child and adult obesity including; walkability, fast food availability, green space size and qualities, active transport routes and school environments. Utilising the Secure Anonymised Information Linkage (SAIL) Databank We linked multi-sectoral data including routine health data, cohort data, administrative data and linked Geographic Information Systems generated metrics at household and school level. The platform will inform policy makers with and facilitate a better understanding of associations between a range of social, health and built environment factors.
ResultsWe have created a range of built environment variables including temporally and age varying walkability indices, viewable greenspace, garden and house size, access to services and parks for 1.5 million households. In the first instance, as part of the BEACHES project, this data has been linked to several health datasets including the Child Measurement Programme (CMP, n=188,800) where initial results have shown that associations between garden size and Body Mass Index in children displays a non-linear negative correlation. We have also created follow-up measures for the CMP using routinely collected general practice data which further enables linking 28,389 height and weight measurements. However, potential bias in these follow-up measures is poorly understood with further work being undertaken to assess usability.
ConclusionThe integrated multi-sectoral data platform approach to linking environmental, administrative, health and cohort data aims to develop insights on a range of public health issues. We are working with a range of stakeholders to develop evidence-based policy initiatives to reduce obesity in Wales.

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

ObjectivesHaving multiple long term health conditions (MLTCs), also known as multimorbidity, is becoming increasingly common as populations age. Understanding how clusters of diseases are likely to lead to other diseases and the effect of multimorbidity on healthcare resource use (HRU) will be of great importance as this trend continues.
ApproachGraph-based approaches, also called network analysis in the literature, have been used previously to study multimorbidity. The use of hypergraphs, which are generalisations of graphs where edges can connect to any number of nodes, and their application to the problem of understanding multimorbidity will be discussed. Analysis using hypergraphs was carried out using a population-scale cohort of people in the Secure Anonymised Information Linkage (SAIL) Databank to find the diseases and disease sets which are most important based on a measure of prevalence and measures of healthcare resource utilisation in secondary care.
ResultsThe most important sets of diseases based on the centrality of a hypergraph weighted by a measure of prevalence featured hypertension, and the most important was hypertension and diabetes. The most important sets of diseases based on the centrality of a hypergraph weighted by a measure of unplanned inpatient HRU were arrhythmia, heart failure and hypertension while for a measure of outpatient HRU the most important set of diseases was diabetes and hypertension.
ConclusionHypergraphs are very flexible and general mathematical objects and there is still a great deal of development that can be done to make them more useful in epidemiological settings and beyond.