BACKGROUND:Childhood obesity is a major global health challenge, projected to affect one in three children worldwide by 2050. While individual and social factors contribute, increasing evidence highlights the built environment as a key determinant in shaping children's behaviours and weight outcomes. Evidence suggests that neighbourhood design, greenspace access, and food retail availability influence diet and physical activity, but most studies rely on small samples or single-domain measures. METHODS:We linked nationwide geographic information systems (GIS) data describing residential neighbourhoods with objectively measured child weight from a national Welsh surveillance programme for children aged 4-5 years. Using multiple indicators-including housing type, garden size, neighbourhood greenness, walkability, access to recreational spaces, and food outlet density - latent class analysis was used to identify distinct "environmental phenotypes." Associations between phenotypes and child weight status were examined using logistic regression. RESULTS:We identified discrete classes of residential environments characterised by varying combinations of built and food environment features. The model with five classes was the best fit overall, with percentage and number of households in each phenotypes: Rural, spacious and isolated 14% (24,266), Suburban 17% (29,324), Deprived and underserved 23% (39,227), Deprived and well-served 32% (53,210), and Dense, coastal and well-connected 13% (21,762). Children living in rural, spacious and isolated neighbourhoods, characterised as those with greater greenspace, private gardens, and walkable layouts, had significantly lower odds of overweight and obesity (OR = 0.89, CI = 0.86-0.93), whereas those in deprived and well-served neighbourhoods, characterised by high-density housing areas with limited greenspace and high fast-food outlet density, had elevated risk (OR = 1.09, CI = 1.06-1.13). These associations remained robust after adjustment for area-level deprivation and rurality. CONCLUSION:Our findings highlight the importance of the residential environment in early childhood obesity risk. Nationally linked GIS and health data enable robust classification of obesogenic environments, informing urban planning and public health strategies to promote healthier, child-friendly neighbourhoods.

Climate change is affecting our world, and the impact of rising temperatures on health is not well understood. Prior work found exposure to heat was associated with reduced gestational age, increased prematurity and smaller birth weights. The goal of the Maternal and Pregnancy Health and Elevated Heat (MAGENTA) project is to determine if these patterns exist in a UK population. Utilising healthcare and environmental exposure data held in the Secure Anonymised Information Linkage (SAIL) Databank we developed a cohort of mothers who were pregnant between 2010 and 2023, and the associated daily maximum temperatures experienced during each pregnancy. Modelled Land Surface Temperature accounts for the effects of the built environment. The primary outcome was time to birth. We used a joint longitudinal and time-to-event model, constructed in a Bayesian framework to capture full parameter uncertainty and fit using the Integrated Nested Laplace Approximation (INLA). The longitudinal process modelled temperature experienced during the pregnancy with linear and quadratic terms for time. Time to birth was modelled using a Cox regression model with a spline baseline hazard, and smoothed at second order. Joint modelling is a flexible, powerful set of tools for understanding associations in healthcare research, and approximation methods such as INLA enable analysis in large-scale electronic health record datasets. Work is ongoing to produce fully adjusted models, which will be used in simulation studies in conjunction with climate change projections to explore the impact of future scenarios to inform mitigation and adaptation strategies.

Background The environments where children live play a critical role in shaping health trajectories, yet comprehensive population-level descriptions of these environments remain limited. Understanding residential environmental characteristics is essential for informing urban planning and public health strategies aimed at creating child-friendly neighbourhoods. Methods We linked nationwide geographic information systems (GIS) data to residential addresses for children in Wales, enabling detailed characterisation of local environments. Indicators included housing type, garden size, neighbourhood greenness (Enhanced Vegetation Index from Landsat 8 imagery), walkability, access to recreational spaces, and food outlet density. Using these measures, we classified neighbourhoods into distinct “environmental phenotypes” representing combinations of built and natural features. Spatial analyses explored variation in these phenotypes across urban and rural areas and by area-level deprivation. Results Five discrete environmental phenotypes emerged, ranging from high-density housing with limited greenspace and high fast-food outlet density to low-density, walkable neighbourhoods with private gardens and abundant greenspace. These classifications reveal substantial geographic variation and highlight clusters of potentially obesogenic environments. Conclusion This work demonstrates the feasibility of using linked GIS and administrative data to generate robust, scalable measures of residential environments for children. Such classifications provide a foundation for future research examining environmental influences on health and for guiding policy interventions to promote healthier, child-friendly communities.

Rates of anxiety and depression among adolescents are rising globally. Exposure to greenness is recognised as a determinant of mental health but little is known about its impacts on adolescent mental health on a population scale. We examined the prospective association between residential greenness from birth to age 12 and subsequent health-care contacts for mood or anxiety disorders during adolescence. We conducted a population-scale longitudinal study using linked health, administrative data, and environmental measures for 831,957 individuals in the Ontario Mental Health and Intersectionality Data Surveillance Cohort within a Trusted Research Environment (ICES). The outcome was diagnosis-specific mental health-related service use for mood or anxiety disorders. Exposure was defined as residential greenness within a 250-metre buffer around each participant's postcode centroid from birth to age 12. Associations between exposure to greenness and mood or anxiety-related service use were estimated using fully adjusted multivariable Cox regression models. In our cohort, 51.3% were female and 422,227 individuals had at least one mood- or anxiety-related service use. Mean greenness exposure was 0.12 (range 0-0.74). Greater exposure to greenness was associated with lower risk of mood or anxiety service use. A statistically significant U-shaped relationship was observed, with the highest risk in quintile 3 (HR 1.09, 95% CI 1.08-1.10) compared with the greenest quintile. Childhood exposure to greenness appears protective against later mood and anxiety-related service use and nonlinear effects warrant further investigation. This evidence can be used by policy and practice for the design of interventions.

Background An unknown proportion of mothers are involved in both the Criminal Justice and Family Justice Systems. Mothers with cross-justice involvement are at risk of losing their children from their care on a temporary or permanent basis. Objectives Speaking directly to the Female Offender Strategy for England and Wales and the imperative to preserve mother-child relationships wherever safe and in the child's best interests, the Child Outcomes for Mothers Facing Trial (COMFT) study aims to uncover mothers' dual system involvement and provide vital insights about caregiver outcomes for children. The project will use administrative data to deliver the first-ever quantitative analyses of mothers and children at the intersection of the criminal and family justice systems. Methods The project will link female defendant records in England and Wales to mother and child family court records (public and private law), as well as demographic data. Data will be accessed through the globally leading Secure Anonymised Information Linkage (SAIL) Databank at Swansea University. The proposal has been developed collaboratively with project partners, committed to transforming justice outcomes for women and children, including the Ministry of Justice (MoJ), the Children and Family Court Advisory and Support Service (Cafcass), Cafcass Cymru and the leading charity, Birth Companions. Formal ethical approval has been granted by the Universities involved. Patient and Public Involvement A unique feature of this project is that from design to completion, a group of mothers with lived experience are directly involved. The 'COMFT-Together' advisory group ensures that the perspectives of women with cross-justice involvement inform this study, and that mothers benefit directly from a shared research role and curriculum facilitated by Birth Companions. Implications As the study is the first of its kind, it will also serve as an international exemplar, relevant to policy makers across the globe wishing to use routine organisational data to inform public services delivery.

Background People with severe mental disorders, including schizophrenia, experience worse physical health and shorter life expectancy. While socio-demographic factors are well established contributors to these disparities, less is known about how environmental exposures differ between individuals with severe mental disorders and the general population. Objective To explore socio-demographic and environmental differences between the general population and individuals with schizophrenia and/or other psychotic disorders (OPD). Methods Anonymised general practice records (2016--2019) for individuals resident in Wales were accessed within the Secure Anonymised Information Linkage (SAIL) Databank. This anonymised health data were linked to small-area level air pollution data for 2016 (PM10, PM2.5, NOx) for individuals who remained at the same residential address during the study period. Individuals with a first relevant diagnosis were identified; age, sex, urban/rural residence, deprivation, and air pollution exposures were compared with the general population. Results Overall, 0.1% (1,784) of the SAIL population had a first recorded diagnosis of schizophrenia/OPD. Of these, 52.7% (941) were male and 47.3% (843) were female. The psychiatric cohort had a higher mean age at study entry than the general population (46.9 vs 42.3 years). The distribution of diagnoses in urban (72.3%) and rural (27.7%) areas closely matched the total population (urban 70.5%, rural 29.5%). Mean air pollutant levels (PM10, PM2.5, NOx) in 2016 were similar between the psychiatric cohort and the general population. A clear socioeconomic gradient was observed: 29.1% of cases resided in the most deprived quintile compared with 15.0% in the least deprived quintile. Conclusions Area-level deprivation was associated with higher prevalence of schizophrenia/OPD, whereas no clear differences were observed by urbanicity or air pollution exposure. Linked population-scale health and environmental data provide valuable evidence which could inform service planning and targeted public health interventions.

The ability to manage ill health and care needs might be affected by who a person lives with. This study examined how the risk of unplanned hospitalisation and transition to living in a care home varied according to household size and co-resident multimorbidity. Here we show results from a cohort study using Welsh nationwide linked healthcare and census data, that employed multilevel multistate models to account for the competing risk of death and clustering within households. The highest rates of unplanned hospitalisation and care home transition were in those living alone. Event rates were lower in all shared households and lowest when co-residents did not have multimorbidity. These differences were more substantial for care home transition. Therefore, living alone or with co-residents with multimorbidity poses additional risk for unplanned hospitalisation and care home transition beyond an individual's sociodemographic and health characteristics. Understanding the mechanisms behind these associations is necessary to inform targeted intervention strategies.

Objectives The aim of this study was to examine associations between maternal heat exposure during pregnancy and birth outcomes. Methods This cross-sectional, retrospective study linked administrative data on all births in Wales during 2022 to maternal heat exposure data. Data were accessed via the Secure Anonymised Information Linkage (SAIL) Databank. Maternal residential histories were anonymously linked to HADUK climate data to quantify trimester-specific heat exposure (days above the 90th percentile). Birth outcomes included gestational age, birth weight, stillbirth, neonatal mortality, Apgar scores, and mode of birth. Generalised linear regression models were employed to explore associations and to adjust for maternal age, ethnicity, and deprivation. This approach linked environmental and administrative data to generate insights into heat exposure effects on pregnant women. Results The analysis included all births in Wales during 2022 (n = 30,553), comprising 15,822 males (51.79%) and 14,731 females (48.21%). The mean gestational age at birth, the primary outcome, was 38.84 weeks (sd = 2.2). Mean birth weight at birth was 3329.4 grams (sd = 614.9). There were 130 stillbirths and 76 cases of neonatal mortality recorded. Exclusion criteria included multiple pregnancy and incomplete maternal address history during pregnancy. Ongoing analysis examines the association between maternal heat exposure during pregnancy and birth outcomes. Adjusting for factors such as maternal age, ethnicity and deprivation aids further understanding of the potential effects of heat exposure on various subgroups of pregnant mothers, which may result in greater health inequalities. Conclusion Pregnant women are vulnerable to the effects of climate change. This study explores the effects of heat exposure on pregnancy outcomes to guide future research. This work has the potential to contribute to evidence-based policymaking and underscores the value of linking environmental to administrative data in addressing global health challenges.

Objectives This study aimed to identify environmental profiles of households with children across Wales and to examine the association between environmental characteristics and children’s weight status at ages 4 and 5. Methods Geographic Information System (GIS) metrics and environmental characteristics were applied to the households of children with a Body Mass Index (BMI) measurement recorded in the Wales Child Measurement Programme (CMP) between 2013 and 2019. BMI UK1990 clinical reference standards were used to categorise children as living with a “healthy” (≥second to <91st centile) or “unhealthy” (≥91st centile) weight. Profiles of home environments were determined using latent class analysis. Characteristics of the best model were examined to determine if they were meaningful qualitatively in the context of children’s environments. Logistic regression evaluated the association between the latent profiles and weight status. Results A total of 167,789 unique households were identified from the residences of 214,947 children who had a BMI measurement in the CMP. These households were separated into five profiles: ‘Rural, spacious and isolated’, ‘Suburban’, ‘Deprived and underserved’, ‘Deprived and well-served’, ‘Dense, coastal, well-connected’. Profile membership was associated with weight status; for example, children residing in ‘deprived, well-served’ areas were 22% more likely to be living with an unhealthy weight compared to children living in ‘rural, spacious and isolated’ areas. Conclusion Findings demonstrate the importance of research examining aggregate effects of children’s environments on unhealthy weight. Policy and practice needs to focus on those features which are modifiable to increase opportunities for healthier lifestyles.

Introduction Pregnant women and their babies are a highly vulnerable population to health effects from air pollution. This scoping review aims to understand the extent and type of evidence concerning the mediating and moderating factors between air pollution and birth outcomes. By gathering and synthesising this evidence, this review aims to identify key concepts, themes and knowledge gaps. In turn, these findings will serve as a valuable resource for researchers and policymakers by highlighting potential pathways and gaps in evidence.Methods and analysis This scoping review protocol is based on the Joanna Briggs Institute (JBI) methodology for scoping reviews and will be reported in full with a Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping review (PRISMA-ScR) flow diagram. This review will search eight databases: Web of Science, Scopus, PubMed, Embase, GreenFILE, CINAHL Ultimate, APA PsycINFO and MIDIRS. Results will be limited to those written or translated into English and peer-reviewed studies with no restriction on publication date. The study selection and data extraction will be completed within the software Covidence by two or more independent reviewers, with conflicts solved by group discussion. The data extracted from this process will include publication details, study characteristics and population characteristics.Ethics and dissemination This study will not collect primary data; therefore, no formal ethical approval is required. The findings will be disseminated to academic and non-academic audiences through conferences, publications and focus groups.Trail registration number Open Science Framework (https://doi.org/10.17605/OSF.IO/6Y2D9).

Background:Childhood obesity is a complex and multifaceted public health issue. Several studies have found that children living in greener neighborhoods have a lower body mass index (BMI); however, evidence on longitudinal exposure remains limited. This study examined the relationship between Enhanced Vegetation Index (EVI), green space, and children's weight status using linked environmental and national health data. Methods:We derived annual EVI values from Landsat 8 satellite imagery (30 m resolution) within 300 m of a child's residence in Wales from 2008 to 2019. Mean EVI exposure was calculated for the 4 years preceding BMI measurement. We utilized 2017 Ordnance Survey Open Greenspace data to identify green spaces within 800 m of a child's residence. BMI obtained from the Child Measurement Programme for Wales (2012/13 to 2018/19) for children aged 4-5 years was used to define healthy versus overweight/obesity. We used logistic regression to evaluate associations between residential greenness, green spaces, and childhood weight status. Results:The final cohort consisted of 200,237 children. A one-unit increase in EVI was associated with a 20% higher likelihood of being overweight or obese (OR = 1.20, 95% CI = 1.05, 1.37). For every additional green space within 800 m, the likelihood of having an unhealthy weight increased by 0.3%. Conclusions:Our findings suggest that EVI and access to green spaces should be interpreted with care, as they may not capture how young children interact with nearby green environments. Future work investigating the impact of greenness and greenspace on child weight status should use measures tailored to more accurately represent age-specific behaviors.

Objectives This study examines the association between residential greenness, access to green space, and childhood Body Mass Index (BMI) on a national population of children in Wales. Using linked environmental and national surveillance data, we assess whether long-term exposure to greener environments is associated with children being overweight or obese. Methods BMI was calculated using height and weight records from the Child Measurement Programme for Wales (2012/13–2018/19). We used Landsat satellite imagery (30-meter resolution) to derive an annual Enhanced Vegetation Index (EVI) within 300m of each child's residence in Wales for 2008–2019. EVI values were averaged over four-years preceding their BMI measurement. Green space access was calculated using Ordnance Survey Open Greenspaces to create a count of green spaces within 800m of each child’s residence. Logistic regression was used to assess associations between EVI, green space, and BMI, adjusting for sociodemographic characteristics. Results The study included 201,698 children aged 4–5 years old. After adjusting for covariates, every unit increase in EVI was associated with a 20% higher likelihood of a child being overweight or obese. For every additional green space within 800m of a child’s home, the likelihood of unhealthy weight increased by 0.3%. Findings suggest that higher EVI levels and greater green space availability do not necessarily translate into healthier BMI outcomes. Conclusion This study explored associations between objective measures of ambient greenness, green space and BMI in a national population of children. Findings demonstrate that higher EVI was associated with greater odds of unhealthy weight, highlighting the complexity of the relationship. EVI should be interpreted carefully as it is a measure of vegetation health and may not capture how children use green environments. BMI is measured at a single time point in Wales (ages 4-5) when children are less independent and therefore these findings may not reflect greenspace’s health benefits in older childhood. Future studies should focus on objective exposures that can accurately reflect children’s interactions with their built environment.

Introduction The prioritisation of acute cases of coronavirus during the pandemic caused significant disruption to non-urgent healthcare services, creating a backlog of undiagnosed and untreated individuals with long-term conditions. Previous research has explored the impact of the pandemic on long-term conditions in Wales, but not the geographic variation or underlying area-level characteristics associated with these changes. Objectives We created the SAIL long-term conditions e-cohort (SLTC cohort) within the Secure Anonymised Information Linkage (SAIL) Databank to describe changes in healthcare service use of individuals living with long-term conditions during the COVID-19 pandemic, and to facilitate future investigations into the underlying reasons for these changes. Methods Individuals were included in the cohort if they interacted with health services with a long-term condition between January 2017 and December 2022. Interactions were identified using primary and secondary care datasets within the SAIL Databank. We linked this interaction level data with individual, residence, and area-level demographic data. We calculated area-level age-sex-standardised rates of interactions, based on an individual's address at the time of interaction, for the 3 years pre-COVID-19 (2017-2019) and during-COVID-19 (2020-2022). Percentage changes in rates between these time periods were calculated, and we investigated the underlying area-level characteristics associated with these differences. Results The SLTC cohort contains 1,277,532 individuals. Age-sex standardised interaction rates varied by Welsh Index of Multiple Deprivation (WIMD) quintiles and Rural-Urban Classification. Areas in the most deprived WIMD quintile had the greatest median percentage decrease (23.5%) in primary care rates of interactions from pre- to during-COVID-19, and the least deprived overall WIMD quintile had the smallest (16.9%). Areas classified as 'Urban city & town in a sparse setting' had the greatest decrease in primary care interactions (29.7%), and `Rural village' areas had the smallest decrease (17.1%). Secondary care rates of interactions showed less variation in rates of interactions between the two time periods. Conclusion We have created a cohort that links area-level characteristics and measures of healthcare resource use, in a study period that covers pre- and during-COVID-19, which will allow researchers to investigate geographic variation of changes in healthcare resource use over this time period and the underlying influences. This cohort can also be further linked to other area-level characteristics of interest, such as travel times to general practices, or access to green space measures.

Background and ObjectivesThe health impacts from changing climate, including increasing frequency, severity, and duration of heat are projected to worsen. The MAGENTA study will explore the impact of heat on pregnancy-related outcomes for deprived communities in Wales and London. ApproachMAGENTA will create linked data cohorts capturing some of the most deprived and diverse communities in Europe to analyse the impact deprivation, ethnicity, heatwaves, and urban heat islands have on pregnancy-related health. A consented data-linked cohort of pregnant women will provide biological samples and undergo direct measurement of themselves and their home environments to understand the heat stress response of pregnant women not acclimatised to heat during their pregnancy. Core to the study is our public involvement strategy which includes pregnant women and women who have recently delivered a baby, co-producing the research and how results are communicated. ResultsOur electronic cohorts cover >10 years of climate and health data and capture all births in Wales >300,000 and >1.1 million in London. We will present how the novel data linkages can work in multiple trusted research environments, approaches for sharing methods in geospatial modelling and statistics across the project and preliminary findings from the study. Findings and ImplicationsThe MAGENTA study will help inform expectant parents, families and policy makers how to prepare for climate change in temperate regions. The comparison between different populations in Wales and London will give insight on where policies and interventions can be generally applied and where they need to be contextually adapted.

Background Child poverty remains a major global concern and a child's experience of deprivation is heavily shaped by where they live and the stability of their local neighbourhood. This study examines frequencies and patterns of residential mobility in children and young people (CYP) at a population level using novel geospatial techniques to assess how often their physical environment changes and to identify geographical variations in social mobility. Methods We used routinely collected administrative records held in the Secure Anonymised Information Linkage (SAIL) Databank for CYP aged under 18 years living in Wales between 2012 and 2022. We calculated the Moran's I statistic to assess the magnitude of Lower layer Super Output Area (LSOA)-level geographic variation in residential mobility and used the Local Indicator of Spatial Association (LISA) to identify clusters of LSOAs where there are higher rates of residential mobility. Results This study included 923,531 CYP, with 58% having moved at least once during the study period. A total number of 1,209,102 house moves were recorded, 59% of which occurred between the ages of 0 and 5 years. Almost 10% of the cohort resided in five or more dwellings before the age of 18 years. In terms of area-level (LSOA) deprivation, 75% of house moves were to areas with the same or higher levels of deprivation, leaving only 25% of house moves that achieved upward social mobility. Clustering of residential mobility was identified predominantly in areas of high deprivation. Conclusion The findings of this study show that residential mobility is linked with socio-economic circumstances and is experienced by over half of CYP in Wales. Understanding where CYP live, their mobility patterns and which areas have high levels of influx and efflux is crucial for policymakers to generate well-informed, targeted and effective child-focused interventions.

There is a lack of studies investigating the effects of green space and biodiversity on mental well-being, across a large study area. Generally, exposure to natural environments promotes better physical health, mental health and well-being. This study investigated associations between publicly accessible green space, biodiversity and mental well-being for individuals living in Wales using routinely collected survey and biodiversity data. This study used the Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) to measure mental well-being. The 2018-19 National Survey for Wales responses containing the WEMWBS scores and socio-demographic factors were linked to green space and biodiversity data in census areas. By utilising Generalised Additive Models this study found that all environmental metrics were associated with mental well-being. However, after adjustment for sociodemographic factors, only bird species richness remained associated with mental well-being, with a highly non-linear relationship. There was little to no evidence of associations between green space or biodiversity when stratifying by income group. When stratified by rural and urban areas, we found bird, plant and total species richness to be associated with mental well-being. Environmental interventions should consider promoting bird species richness in urban areas which may benefit mental well-being. Future areas of research could include longitudinal studies to explore causal links between green spaces, biodiversity and mental well-being, utilising individual-level exposure.

Background Exposure to green space can protect against poor health through a variety of mechanisms. However, there is heterogeneity in methodological approaches to exposure assessments which makes creating effective policy recommendations challenging. Objective Critically evaluate the use of a satellite-derived exposure metric, the Enhanced Vegetation Index (EVI), for assessing access to different types of green space in epidemiological studies. Methods We used Landsat 5–8 (30 m resolution) to calculate average EVI for a 300 m radius surrounding 1.4 million households in Wales, UK for 2018. We calculated two additional measures using topographic vector data to represent access to green spaces within 300 m of household locations. The two topographic vector-based measures were total green space area stratified by type and average private garden size. We used linear regression models to test whether EVI could discriminate between publicly accessible and private green space and Pearson correlation to test associations between EVI and green space types. Results Mean EVI for a 300 m radius surrounding households in Wales was 0.28 (IQR = 0.12). Total green space area and average private garden size were significantly positively associated with corresponding EVI measures (β = < 0.0001, 95% CI: 0.0000, 0.0000; β = 0.0001, 95% CI: 0.0001, 0.0001 respectively). In urban areas, as average garden size increases by 1 m 2 , EVI increases by 0.0002. Therefore, in urban areas, to see a 0.1 unit increase in EVI index score, garden size would need to increase by 500 m 2 . The very small β values represent no ‘measurable real-world’ associations. When stratified by type, we observed no strong associations between greenspace and EVI. Impact It is a widely implemented assumption in epidiological studies that an increase in EVI is equivalent to an increase in greenness and/or green space. We used linear regression models to test associations between EVI and potential sources of green reflectance at a neighbourhood level using satellite imagery from 2018. We compared EVI measures with a ‘gold standard’ vector-based dataset that defines publicly accessible and private green spaces. We found that EVI should be interpreted with care as a greater EVI score does not necessarily mean greater access to publicly available green spaces in the hyperlocal environment.

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.

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.