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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.

Background The natural environment offers a plethora of health benefits and may mitigate the risk of contracting SARS-CoV-2. However, they are also sites where interpersonal contact is frequent, amplifying the risk of transmission. Previous research has typically focused on green space or a measure of greenness. Greenness is often associated with fewer COVID-19 infections, whereas associations with green space are mixed. Very few studies have investigated blue spaces. To our knowledge, no studies have investigated associations between greenness, green space, and blue space with COVID-19 for a national population. Objectives & Approach We aimed to investigate the impacts of exposure to the natural environment on the likelihood of testing positive for COVID-19 in small areas across Great Britain. We derived several measures of green space, greenness and blue space using data from Ordnance Survey and the Google Earth Engine. These measures were linked to the COVID-19 Infection Survey in a Trusted Research Environment. We used mixed-effects logistic regression to account for the multiple tests taken by each participant, adding a random effect for participant ID. We fit univariate models for each predictor and multivariate models for each environmental exposure, adjusting for sex, age, ethnicity, household occupancy, rurality, population density and deprivation. Results The survey contained test results from a random and representative sample of over 500,000 participants. Approximately 11 million tests were taken by the cohort across three years. Conclusions & Implications The results of this study will inform planning for the next pandemic.

Background Australia 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. Methods We 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. Results Interviews 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. Conclusion To 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.

Background and Objectives The 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. Approach MAGENTA 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. Results Our 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 Implications The 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 The 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.

Abstract Background Measurement of multimorbidity in research is variable, including the choice of the data source used to ascertain conditions. We compared the estimated prevalence of multimorbidity and associations with mortality using different data sources. Methods A cross-sectional study of SAIL Databank data including 2,340,027 individuals of all ages living in Wales on 01 January 2019. Comparison of prevalence of multimorbidity and constituent 47 conditions using data from primary care (PC), hospital inpatient (HI), and linked PC-HI data sources and examination of associations between condition count and 12-month mortality. Results Using linked PC-HI compared with only HI data, multimorbidity was more prevalent (32.2% versus 16.5%), and the population of people identified as having multimorbidity was younger (mean age 62.5 versus 66.8 years) and included more women (54.2% versus 52.6%). Individuals with multimorbidity in both PC and HI data had stronger associations with mortality than those with multimorbidity only in HI data (adjusted odds ratio 8.34 [95% CI 8.02-8.68] versus 6.95 (95%CI 6.79-7.12] in people with ≥ 4 conditions). The prevalence of conditions identified using only PC versus only HI data was significantly higher for 37/47 and significantly lower for 10/47: the highest PC/HI ratio was for depression (14.2 [95% CI 14.1–14.4]) and the lowest for aneurysm (0.51 [95% CI 0.5–0.5]). Agreement in ascertainment of conditions between the two data sources varied considerably, being slight for five (kappa

Abstract Natural environments can promote well-being through multiple mechanisms. Many studies have investigated relationships between residential green/blue space (GBS) and well-being, fewer explore relationships with actual use of GBS. We used a nationally representative survey, the National Survey for Wales, anonymously linked with spatial GBS data to investigate associations of well-being with both residential GBS and time in nature (N = 7631). Both residential GBS and time spent in nature were associated with subjective well-being. Higher green-ness was associated with lower well-being, counter to hypotheses (predicting the Warwick and Edinburgh Mental Well-Being Scale (WEMWBS): Enhanced vegetation index β = − 1.84, 95% confidence interval (CI) − 3.63, − 0.05) but time spent in nature was associated with higher well-being (four hours a week in nature vs. none β = 3.57, 95% CI 3.02, 4.13). There was no clear association between nearest GBS proximity and well-being. In support of the equigenesis theory, time spent in nature was associated with smaller socioeconomic inequalities in well-being. The difference in WEMWBS (possible range 14–70) between those who did and did not live in material deprivation was 7.7 points for those spending no time in nature, and less at 4.5 points for those spending time in nature up to 1 h per week. Facilitating access and making it easier for people to spend time in nature may be one way to reduce socioeconomic inequalities in well-being.

BackgroundRoutine 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.MethodsWe 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.ResultsWe 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.ConclusionsRecords 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.

Background Cross-sectional evidence suggests that living near green and blue spaces benefits mental health; longitudinal evidence is limited. Objectives To quantify the impact of changes in green and blue spaces on common mental health disorders, well-being and health service use. Design A retrospective, dynamic longitudinal panel study. Setting Wales, UK. Participants An e-cohort comprising 99,682,902 observations of 2,801,483 adults (≥ 16 years) registered with a general practice in Wales (2008–2019). A 5312-strong ‘National Survey for Wales (NSW) subgroup’ was surveyed on well-being and visits to green and blue spaces. Main outcome measures Common mental health disorders, general practice records; subjective well-being, Warwick–Edinburgh Mental Well-being Scale. Data sources Common mental health disorder and use of general practice services were extracted quarterly from the Welsh Longitudinal General Practice Dataset. Annual ambient greenness exposure, enhanced vegetation index and access to green and blue spaces (2018) from planning and satellite data. Data were linked within the Secure Anonymised Information Linkage Databank. Methods Multilevel regression models examined associations between exposure to green and blue spaces and common mental health disorders and use of general practice. For the National Survey for Wales subgroup, generalised linear models examined associations between exposure to green and blue spaces and subjective well-being and common mental health disorders. Results and conclusions Our longitudinal analyses found no evidence that changes in green and blue spaces through time impacted on common mental health disorders. However, time-aggregated exposure to green and blue spaces contrasting differences between people were associated with subsequent common mental health disorders. Similarly, our cross-sectional findings add to growing evidence that residential green and blue spaces and visits are associated with well-being benefits: Greater ambient greenness (+ 1 enhanced vegetation index) was associated with lower likelihood of subsequently seeking care for a common mental health disorder [adjusted odds ratio (AOR) 0.80, 95% confidence interval, (CI) 0.80 to 0.81] and with well-being with a U-shaped relationship [Warwick–Edinburgh Mental Well-being Scale; enhanced vegetation index beta (adjusted) –10.15, 95% CI –17.13 to −3.17; EVI2 beta (quadratic term; adj.) 12.49, 95% CI 3.02 to 21.97]. Those who used green and blue spaces for leisure reported better well-being, with diminishing extra benefit with increasing time (Warwick–Edinburgh Mental Well-being Scale: time outdoors (hours) beta 0.88, 95% CI 0.53 to 1.24, time outdoors2 beta −0.06, 95% CI −0.11 to −0.01) and had 4% lower odds of seeking help for common mental health disorders (AOR 0.96, 95% CI 0.93 to 0.99). Those in urban areas benefited most from greater access to green and blue spaces (AOR 0.89, 95% CI 0.89 to 0.89). Those in material deprivation benefited most from leisure time outdoors (until approximately four hours per week; Warwick–Edinburgh Mental Well-being Scale: time outdoors × in material deprivation: 1.41, 95% CI 0.39 to 2.43; time outdoors2 × in material deprivation −0.18, 95% CI −0.33 to −0.04) although well-being remained generally lower. Limitations Longitudinal analyses were restricted by high baseline levels and limited temporal variation in ambient greenness in Wales. Changes in access to green and blue spaces could not be captured annually due to technical issues with national-level planning datasets. Future work Further analyses could investigate mental health impacts in population subgroups potentially most sensitive to local changes in access to specific types of green and blue spaces. Deriving green and blue spaces changes from planning data is needed to overcome temporal uncertainties. Funding This project was funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme (Project number 16/07/07) and will be published in full in Public Health Research; Vol. 11, No. 10. Sarah Rodgers is part-funded by the NIHR Applied Research Collaboration North West Coast. Plain language summary We investigated whether people who live near or visit green (parks, woodlands) and blue (riversides, beaches) spaces have fewer common mental health disorders (anxiety or depression), and better well-being. We considered whether changes in the amount of green and blue space around the home affected people’s mental health. We assessed the availability of local green and blue spaces. Annual exposure and access to local green and blue spaces were extracted from planning and satellite data. We linked these data to anonymised health records of 2,801,483 adults registered with a general practice from 2008 to 2019, and to survey answers about leisure visits to natural environments and well-being. We found: people who lived in greener and bluer areas were less likely to seek help for a common mental health disorder than those in less green or blue areas, with those living in the most deprived areas benefiting the most people who used green and blue spaces for leisure, especially those with the greatest levels of deprivation, had better well-being and were less likely to seek help for common mental health disorders no evidence that changing amounts of green and blue space affected how likely people were to seek help for common mental health disorders; this may be because we found mostly small changes in green and blue space, and we may not have allowed enough time between moving home and recording mental health. We found evidence for relationships between green and blue space and mental health. However, some analyses were restricted due to lack of data on changes in green and blue spaces. An important finding was that people in deprived communities appear to benefit the most. Provision of green and blue spaces could be a strategy to improve the mental health of people living in disadvantaged areas. Scientific summary Background Cross-sectional evidence suggests that living close to and spending time in and around green and blue spaces (GBS, e.g. parks, gardens, ponds, lakes, rivers) is associated with higher levels of well-being and fewer mental health problems. Some of these benefits could result from living near GBS; others (e.g. physical activity) are attributed to using GBS. There are few longitudinal studies examining whether changes in GBS affect subsequent mental health. Existing studies have largely had small cohorts, assessed change over a short time, did not investigate socioeconomic inequalities in effects and neglected to consider that some health benefits depend on using GBS rather than simple exposure. Objectives We examined the research questions: Is a greater residential exposure and access to GBS associated with the likelihood of seeking help for a common mental health disorder (CMD) in general practice? Is the association between exposure, or access to, GBS and seeking help for a CMD modified by socioeconomic disadvantage and moving home? Is a greater residential exposure associated with the likelihood of using general practice services? Is greater residential GBS exposure and access to GBS associated with higher subjective well-being or a lower likelihood of seeking help for CMD, and does intentional use of GBS for leisure mediate these associations? Are associations between residential GBS exposure and access to GBS and use and subjective well-being mediated by walking-specific physical activity and modified by socioeconomic disadvantage? Methods We used a dynamic longitudinal panel study design to retrospectively examine natural and non-random variation in GBS exposure during an 11-year period, with individual linkage to survey responses. Data sources We constructed an electronic cohort of the population of Wales using data from the Welsh Demographic Service Dataset. Household-level ambient greenness was determined from satellite imagery, access to GBS from planning records, CMD and service use from Welsh Longitudinal General Practice Dataset (WLGPD) and for a subgroup, well-being and behaviour responses to the National Survey for Wales (NSW, 2016/17 and 2018/19). Data are held in the Secure Anonymised Information Linkage (SAIL) databank. Inclusion criteria The cohort included everyone aged 16 years and over registered with a general practitioner (GP) providing patient records to the SAIL databank between January 2008 and October 2019 (‘the study period’). Those not registered with an appropriate GP, not having a Welsh residential address between January 2008 and October 2019, and not having sex/week of birth recorded were excluded from the study sample. Exposures Our primary environmental exposure was ambient greenness, measured yearly (2008–19) by mean enhanced vegetation index (EVI) averaged over a 300 m circular buffer centred on each residence. Our secondary environmental exposure was the potential for an individual to access GBS along paths/roads within 1600 m of each home, modelled for 2018. We captured changes only when individuals moved home. For research questions 1–3, GBS access was quantified by the number of potentially accessible GBS. For research questions 4–5, access was quantified as the proximity to the nearest GBS. Outcome measures For research questions 1–3, our primary outcome was seeking help for CMD in general practice, identified from practice records using a validated algorithm. Our secondary outcome was the quarterly count of general practice events. For research questions 4 and 5, our primary outcome was subjective well-being, measured by the Warwick–Edinburgh Mental Well-being Scale (WEMWBS) in the NSW. Secondary outcomes were life satisfaction (NSW) and seeking help for CMD in general practice. Self-reported time outdoors on recreational visits to GBS in Wales and walking-specific physical activity were identified (NSW). Statistical analysis Research questions 1–3 were addressed using complementary approaches: We first evaluated associations between cumulative EVI exposure and seeking help for CMD in general practice using multivariate logistic regression. Follow-up stopped when help was first sought for CMD or at the end of the study period. This time aggregated design allowed us to consider environmental exposure prior to seeking help for CMD in general practice and included differences between people. We conducted stratified analyses according to area-level deprivation (Welsh Index of Multiple Deprivation, WIMD) quintiles, home moves (none/one/more than one), history of seeking care for CMD in general practice (before January 2008) and urban-rural home location. Next, we assessed associations between longitudinal change in GBS exposures for the same individuals, and their subsequent likelihood of seeking help for CMD (primary outcome) using multivariate logistic regression. For the association with general practice services use (secondary outcome) we used Poisson regression. We used a panel design with a multilevel model structure (quarterly observations nested within individuals). We accounted for clustering of observations over time (using random effects), and unbalanced data (number of observations varying for individuals). Change in GBS exposure was modelled as a continuous variable relative to previous time periods. For people who did not move home, we modelled EVI change categories to examine in situ exposure changes. Stratified analyses were conducted for quintiles of WIMD and number of home moves. For research questions 4–5 we assessed whether GBS exposures were related to WEMWBS, life satisfaction and seeking help for CMD, and whether there was moderation by socioeconomic disadvantage. We examined whether the association between GBS exposures and WEMWBS, life satisfaction and seeking help for CMD, were mediated by time spent outdoors. Generalised additive models were used to identify any non-linear associations and inform subsequent generalised linear regression. When predicting WEMWBS, linear and quadratic terms for EVI were included and proximity to nearest GBS was categorised. For seeking help for CMD, EVI was categorised, and proximity was continuous. To assess moderation by socioeconomic disadvantage, we included interactions between material deprivation and the GBS exposures. Results The cohort included 2,801,483 individuals (99,682,902 observations). A total of 816,242 individuals (29.1%) sought help for CMD from their general practice at least once in the study period. Research question 1: Is a greater residential exposure and access to GBS associated with the likelihood of seeking health for CMDs in general practice? People who lived in homes surrounded by more ambient greenness (+ 0.1 EVI) were associated with 20% lower odds of seeking help for CMD in general practice [adjusted odds ratio (AOR) 0.80, 95% confidence interval (CI) 0.80 to 0.81]. More potentially accessible GBS (equivalent to 236 more GBS) were associated with 7% lower odds of seeking help for CMD (AOR 0.93, 95% CI 0.93 to 0.93). Every additional 360 m (0.1 unit) to the nearest GBS was associated with 5% higher odds of seeking help for CMD (AOR 1.05, 95% CI 1.04 to 1.05). People experiencing an increase of 0.1 mean EVI was not associated with seeking help for CMD in general practice (exposure change relative to: previous quarter/year/baseline AOR 1.00, 95% CI 1.00 to 1.00). Among the 1,611,581 people who did not move home (57.5% of the cohort), increases or decreases of ≥ 0.15 EVI relative to baseline were not associated with seeking help for CMD (AOR for ≥ +0.15 EVI 1.00, 95% CI 0.99 to 1.00; AOR for ≥ −0.15 EVI 1.00, 95% CI 1.00 to 1.01). Among those who moved home at least once, an increase in the number of potentially accessible GBS was not associated with seeking help for CMD in general practice (GBS increase relative to previous quarter/year/baseline: AOR 1.00, 95% CI 1.00 to 1.00). Research question 2: Is the association between exposure, or access to, GBS and seeking help for a CMD modified by socioeconomic disadvantage and moving home? Living in a home with greater EVI (+ 0.1) and more accessible GBS (+ 236) was associated with lower odds of seeking help for CMD in general practice in all deprivation quintiles and whether people moved. The odds of seeking help were lower for those who did not move (AOR non-movers 0.77, 95% CI 0.77 to 0.78; AOR moved > once 0.92, 95% CI 0.91 to 0.93). There was no evidence that EVI or GBS changes were associated with seeking help for a CMD for people living in either the most or least deprived urban areas. An increase of 0.1 mean EVI (relative to baseline) was associated with slightly lower odds of seeking help for CMD for people who moved home more than once (AOR 0.99, 95% CI 0.99 to 0.99) but not for those who moved once or had not moved. Association between different GBS exposures and seeking help for CMD in general practice were modified by urban–rural home location and a history of seeking help for CMD. Greater EVI was associated with lower odds of seeking help for a CMD for those living in rural areas (AOR 0.59, 95% CI 0.58 to 0.59) compared with urban areas (AOR 0.85, 95% CI 0.84 to 0.85). More potentially accessible GBS were only associated with lower odds of seeking help for CMD for those living in urban areas (AOR 0.89, 95% CI 0.89 to 0.89). In rural areas more GBS access was associated with higher odds of seeking help for CMD (AOR 1.10, 95% CI 1.08 to 1.12). More ambient greenness (+ 0.1 EVI) was associated with lower odds of seeking help for a CMD for those who had historically sought help for CMD (before January 2008) than people who had not (AOR 0.68 vs. 0.84). Research question 3: Is a greater residential exposure associated with the likelihood of using GP services? There was no evidence that an increase in mean EVI was associated with the number of general practice event days/quarter (adjusted incidence rate ratio relative to baseline: 1.00, 95% CI 1.00 to 1.00). Research question 4: Is greater residential GBS exposure and access to GBS associated with higher subjective well-being, or a lower likelihood of seeking help for CMD in general practice, and are these associations mediated by intentional use of GBS for leisure? Among NSW respondents with outcome measures (n = 5971) EVI was significantly related to well-being as measured by WEMWBS with a U-shaped relationship (linear regression; EVI −10.15, 95% CI −17.13 to −3.17; EVI2 12.49, 95% CI 3.02 to 21.97) and weekly time outdoors (linear relationship; EVI 43.41, 95% CI 6.85 to 79.97). Time outdoors was significantly positively related to WEMWBS [time outdoors (hours) 0.88, 95% CI 0.53 to 1.24, time outdoors (hours)2 −0.06, 95% CI −0.11 to −0.01] and life satisfaction [linear relationship; time outdoors (hours) 0.06, 95% CI 0.04 to 0.07]. There was no evidence that time outdoors mediated the relationship between EVI and WEMWBS/life satisfaction. EVI was not associated with CMD (EVI 0.4 to 0.8 vs.

Summary: Background: Living in greener areas, or close to green and blue spaces (GBS; eg, parks, lakes, or beaches), is associated with better mental health, but longitudinal evidence when GBS exposures precede outcomes is less available. We aimed to analyse the effect of living in or moving to areas with more green space or better access to GBS on subsequent adult mental health over time, while explicitly considering health inequalities. Methods: A cohort of the people in Wales, UK (≥16 years; n=2 341 591) was constructed from electronic health record data sources from Jan 1, 2008 to Oct 31, 2019, comprising 19 141 896 person-years of follow-up. Household ambient greenness (Enhanced Vegetation Index [EVI]), access to GBS (counts, distance to nearest), and common mental health disorders (CMD, based on a validated algorithm combining current diagnoses or symptoms of anxiety or depression [treated or untreated in the preceding 1-year period], or treatment of historical diagnoses from before the current cohort [up to 8 years previously, to 2000], where diagnosis preceded treatment) were record-linked. Cumulative exposure values were created for each adult, censoring for CMD, migration out of Wales, death, or end of cohort. Exposure and CMD associations were evaluated using multivariate logistic regression, stratified by area-level deprivation. Findings: After adjustment, exposure to greater ambient greenness over time (+0·1 increased EVI on a 0–1 scale) was associated with lower odds of subsequent CMD (adjusted odds ratio 0·80, 95% CI 0·80–0·81), where CMD was based on a combination of current diagnoses or symptoms (treated or untreated in the preceding 1-year period), or treatments. Ten percentile points more access to GBS was associated with lower odds of a later CMD (0·93, 0·93–0·93). Every additional 360 m to the nearest GBS was associated with higher odds of CMD (1·05, 1·04–1·05). We found that positive effects of GBS on mental health appeared to be greater in more deprived quintiles. Interpretation: Ambient exposure is associated with the greatest reduced risk of CMD, particularly for those who live in deprived communities. These findings support authorities responsible for GBS, who are attempting to engage planners and policy makers, to ensure GBS meets residents' needs. Funding: National Institute for Health and Care Research Public Health Research programme.

Reducing the burden of falls and fall-related admissions to hospital and care homes is an important policy area. Falls cause significant injury leading to a reduced quality of life. We wanted to know if the environment around people’s homes changes the risk of falls for older people in Wales. We linked routinely collected, anonymised health data on frailty, dementia, hospital admissions, care home admission, and A&E attendance in the Secure Anonymised Information Linkage Databank. We also linked individual level demographic data and household level neighbourhood walkability and accessibility metrics detailing the built environment (e.g. access to services, greenspace). We capture change in the built environment and house moves between January 2010 and December 2017. Using unadjusted and adjusted cox regression models we will assess how the risk and severity of a fall changes in relation to the built environment. We have created a dynamic national e-cohort linking data on frailty, dementia, hospital admissions, care home admission, A&E attendance and built environment measures for people living in Wales aged 60 and above between January 2010 and December 2017. At the conference we will report summary statistics for each quarter as well as the overall population. We will also report unadjusted cox model odds ratios, as well as fully adjusted models. We will adjust for age, sex, urbanicity (urban/rural), deprivation (WIMD), dementia diagnosis, and seasonal weather trends. We will also report odds ratios for our stratification analysis where we will investigate whether associations vary by urbanicity and deprivation. With an aging population, it is becoming more important to help the current and future older population age healthily, preventing adverse health outcomes before they happen. This research will help guide policy and resource allocation to support people staying at home and have a more fulfilling life for longer.

Objectives Active travel to school (ATS), such as walking and cycling, not only reduces carbon emissions and air pollution but also contributes to a myriad of health benefits. Understanding the ‘potential’ for ATS across Wales is poorly understood yet vital to inform policy and practice aimed at increasing ATS. Methods Using geospatial techniques, network distances have been calculated for all residences to all schools in Wales. Distances for each residence were de-identified and imported into the Secure Anonymised Information Linkage (SAIL) databank using SAIL’s split file approach. Within SAIL, anonymised distances were linked to encrypted school locations and a cohort of approximately 440,000 children attending a state school in Wales as recorded in the Education Wales (EDUW) dataset. Travel distance to school was subsequently filtered for each child within our cohort. Results The incorporation of geospatial network derived origin-destination pairs (n=2,205,000,000) allows us to explore how households can engage in ATS, and how this varies by primary/secondary school, welsh-speaking, urban/rural, and socioeconomic status. Initially we will present findings based on distance alone, with future refinements to encompass other factors influencing ATS including: active travel routes, road safety, and topography. We will link our results to anonymised ATS survey responses in SAIL, to calculate specific ATS distance thresholds by age, deprivation, and urban/rural status, including social indicators such as household composition. Conclusion Currently there is limited local or national comparable information on the potential for ATS in Wales. This baseline information is urgently needed to inform ATS policy and planning, and to ensure appropriate ATS interventions are prioritised for schools and communities where need is greatest.

BackgroundMultimorbidity is one of the greatest challenges facing health and social care systems globally. It is associated with high rates of health service use, adverse healthcare events, and premature death. Despite its importance, little is known about the effects of contextual determinants such as household and area characteristics on health and care outcomes for people with multimorbidity. This study protocol presents a plan for the examination of associations between individual, household, and area characteristics with important health and social care outcomes.MethodsThe study will use a cross-section of data from the SAIL Databank on 01 January 2019 and include all people alive and registered with a Welsh GP. The cohort will be stratified according to the presence or absence of multimorbidity, defined as two or more long-term conditions. Multilevel models will be used to examine covariates measured for individuals, households, and areas to account for social processes operating at different levels. The intra-class correlation coefficient will be calculated to determine the strength of association at each level of the hierarchy. Model outcomes will be any emergency department attendance, emergency hospital or care home admission, or mortality, within the study follow-up period.DiscussionHousehold and area characteristics might act as protective or risk factors for health and care outcomes for people with multimorbidity, in which case results of the analyses can be used to guide clinical and policy responses for effective targeting of limited resources.

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.

Objectives In 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. Approach We 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. Results We 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. Conclusion The 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.

Objectives Physical 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. Approach Our 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. Results Our 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). Conclusion Our 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.

Objectives Mental 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. Approach Our 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. Results Homes 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. Conclusion Our 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.

Abstract Background Inaccurately modelled environmental exposures may have important implications for evidence-based policy targeting health promoting or hazardous facilities. Travel routes modelled using GIS generally use shortest network distances or Euclidean buffers to represent journeys with corresponding built-environment exposures calculated along these routes. These methods, however, are an unreliable proxy for calculating child built-environment exposures as child route choice is more complex than shortest network routes. Methods We hypothesised that a GIS model informed by characteristics of the built-environment known to influence child route choice could be developed to more accurately model exposures. Using GPS-derived walking commutes to and from school we used logistic regression models to highlight built-environment features important in child route choice (e.g. road type, traffic light count). We then recalculated walking commute routes using a weighted network to incorporate built-environment features. Multilevel regression analyses were used to validate exposure predictions to the retail food environment along the different routing methods. Results Children chose routes with more traffic lights and residential roads compared to the modelled shortest network routes. Compared to standard shortest network routes, the GPS-informed weighted network enabled GIS-based walking commutes to be derived with more than three times greater accuracy (38%) for the route to school and more than 12 times greater accuracy (92%) for the route home. Conclusions This research advocates using weighted GIS networks to accurately reflect child walking journeys to school. The improved accuracy in route modelling has in turn improved estimates of children’s exposures to potentially hazardous features in the environment. Further research is needed to explore if the built-environment features are important internationally. Route and corresponding exposure estimates can be scaled to the population level which will contribute to a better understanding of built-environment exposures on child health and contribute to mobility-based child health policy.

Introduction Green and blue spaces (GBS), such as parks, woodlands, rivers, and beaches, are thought to be important for mental health and wellbeing. Our longitudinal cohort contains objective household-level environment data linked at the invidual level to routinely recorded mental health data, augmented with cross sectional self-reported health behaviours, including leisure visits to the outdoors. Objectives and Approach Our overall approach will evaluate if residential proximity to GBS is associated with mental health and wellbeing, and if any associations are modified by visits to outdoors spaces following individual-level data linkage. Here, we examined cross-sectional survey data on time spent visiting nature outdoors. Wellbeing outcomes were assessed using self-reported scores. Data were analysed using generalised additive models in the SAIL Databank. Results Using a sample of National Survey for Wales respondents (2016/17, n=3,481), over 40% of adults in Wales reported spending less than 30 minutes outdoors each week. Weekly time outdoors was positively associated with wellbeing (p=0.007) and life satisfaction (p=0.03) having adjusted for potential confounders including, age, rurality, loneliness, employment status. Confidence intervals varied along the fitted GAM model. Models using a second wave of survey data (n≈7,000), anonymously record-linked to residential environment and health data will explore these associations further. Conclusion A previous study based in England (White et al. 2019) found an upper wellbeing benefit threshold of 2 hours per week for time spent in nature. This was not apparent in our preliminary models, but may be revealed in further analyses. We will next incorporate longitudinal mental health and environmental data for 2 million adults living in Wales, UK. Linking to ambient and accessible residential GBS, while taking into account changes due to migration and actual visits, will allow us to provide valuable guidance to local government, who are often responsible for provisioning and maintaining outdoor facilities.

Introduction A growing evidence base indicates health benefits are associated with access to green-blue spaces (GBS), such as beaches and parks. However, few studies have examined associations with changes in access to GBS over time. Objectives and Approach We have linked cross-sector data collected within Wales, United Kingdom, quarterly from 2008 to 2019, to examine the impact of GBS access on individual-level well-being and common mental health disorders (CMD). We created a longitudinal dataset of GBS access metrics, derived from satellite and administrative data sources, for 1.4 million homes in Wales. These household-level metrics were linked to individuals using the Welsh Demographic Service Dataset within the Secure Anonymised Information Linkage (SAIL) Databank. Linkage to Welsh Longitudinal General Practice data within SAIL enabled us to identify individual-level CMD over time. We also linked individual-level self-reported GBS use and well-being data from the National Survey for Wales (NSW) to routine data for cross-sectional survey participants. Results We created a longitudinal cohort panel capturing all 2.84 million adults aged 16+ living in Wales between 2008 and 2019 and with a general practitioner (GP) registration. Individual-level health data and household-level environmental metrics were linked for each quarter an individual is in the study. Household addresses were linked to 97% of the cohort, creating 110+ million rows of anonymously linked cross-sector data. The cohort provides an average follow-up period of 8 years, during which 565,168 (20%) adults received at least one CMD diagnosis or symptom. Conclusion / Implications This example of multi-sectoral data linkage across multiple environmental and administrative data sources has created a rich data source, which we will use to quantify the impact of changes in GBS access on individual–level CMD and well-being. This evidence will inform policy in the areas of health, planning and the environment.

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

Background with rationale Spending time in green-blue spaces (GBS) is beneficial for mental health and wellbeing. There are few longitudinal studies, and definitions of GBS differ within academic studies and between policy, practice and research. Main Aim Quantify the impact of longitudinal exposure to GBS on wellbeing and common mental health disorders, for a national population (2008-2018) for use in a population-wide natural experiment. Methods We co-produced a GBS typology with planners and policy makers at a day-long workshop using validated public participation methods. Using this typology, we built a national, longitudinal GBS dataset created from local government audits and satellite data for 1.4 million homes in Wales, UK. Results produced a nested national typology to define GBS that built on previous academic literature and considered policy and local government planning priorities. The typology differentiated between inland and coastal GBS and facilities available at the GBS e.g. benches, public toilets etc. We created a national, longitudinal dataset of GBS and a cross-sectional dataset of household-level access to GBS for 2018. Access to GBS varied by socio-economic status, urban/rural classification and type of GBS. Conclusion We worked with policy and planners to produce a typology that will enable us to translate our findings to be used in evidence based policy and planning. We will use the dataset to create quarterly household access to GBS for eleven years (2008-2018). We will link GBS access scores to individual level mental health for 1.7 million people with primary care data and survey data (n = ~12,000) on wellbeing. The results from the wider study will inform the planning and management of GBS in urban and rural environments and contribute to international work on impacts of the built environment on mental health and wellbeing.

Background The use of Geographic Information Science in administrative and health data research provides researchers and policymakers with powerful modelling capabilities when exploring spatial variations in health and social outcomes. However, there are challenges around using spatial data and associated modelling techniques in terms of computing power, skills, data quality and disclosure controls. Main Aim In this case study, we describe our approach to overcoming some of these problems in conjunction with the Secure Anonymised Information Linkage Databank (SAIL). Approach The UK Secure E-Research Platform (UKSeRP) is a suite of technologies which can be linked together to provide a secure system to support a particular use case or data resource. We have taken this foundation and built a Geographic Information UKSeRP which is solely focused on the storage, manipulation and generation of spatial data and models to support administrative and health research. The GIS system is built around the Unique Property Reference Number (UPRN) which can be anonymised into a Residential Anonymised Linking Field (RALF) and provides a precise location of households across the UK. This system allows the user to generate high-resolution spatiotemporal models of geographically varying phenomena (e.g. access to services, fast food, alcohol, greenspace and pollution) in a secure managed environment. To support the geographic data stored in the platform there is a range of GIS tools (Python, R, QGIS, PostGIS, OSRM) and standardised methods available to users which sit alongside a data catalogue. Access to the platform is managed via an information governance review process on a project by project basis to ensure best practice in disclosure control and integrity. Conclusion Spatial data and GIS techniques are an important resource for academic and policy researchers. The GRAPHITE platform demonstrates best practice for using spatial data when used in conjunction with sensitive data.

Introduction The dose-response relationship between exposure to food and BMI, has not been widely investigated. Furthermore, household-level, GIS-generated food environment exposure scores have not previously been linked with individual-level, anonymised BMI data. This study linked GIS-generated residential level environmental exposure scores with historical anonymised, health data held in the SAIL databank. Objectives and Approach Household level GIS-generated exposure data for a region of about 1 million people were anonymised into SAIL using the ‘split-file’ method. All individuals living in the 633,884 homes at the time of data collection (2009-2010) were flagged using a population register. Separately, a cohort of 1147, 11-13 year old pupils were linked to their health data before joining to their environmental exposures. Two subgroups were established within the linked dataset: individuals living at 4.8km or less from the school they attended were assumed to walk to school (“walkers”) and pupils who lived further than 4.8km were flagged as “non-walkers”. Results A total of 916 pupils (80%) were successfully linked to the population register. The BMIs were collected in 2009-2010, but more recent data is likely to have a greater proportion of successful links (more recently, 97% of individuals and their health data have been linked to their home and exposures in SAIL). Erroneous BMIs were removed (n=33, 2.9%). Anonymised exposure data were linked with the remaining 883 (77%) individuals. The dataset contained 352 males (39.9%) and 531 females (60.1%); of these, 38% were from deprived areas and 62% lived in affluent areas. There were 431 (48.8%) pupils in the “walkers” group and 452 (51.2%) in the “non-walkers” group. In the “walkers” group, 13% were obese compared with 22% of “non-walkers” (chi-squared = 12.3, p

Introduction Modelling the daily exposure environment provides evidence for policy and practice. However, the dose-response relationship between exposure to food environments and obesity has not been widely investigated. This study investigated whether increased retail food environment (RFE) exposure in children was associated with a larger body mass index (BMI). Objectives and Approach Individually tailored environmental exposures were calculated in a GIS for home and school locations, and modelled walking routes to and from school. Exposures were linked to individual level health data in the SAIL databank for a cohort of individuals aged 11-13 years from south Wales who had BMI measurements. A fully adjusted multilevel regression model was fitted to investigate the association of RFE exposure with BMI. Based on the distance individuals lived from school, we investigated differences between children who have the potential to walk to school (“walkers” lived 4.8km). Results Home exposure and exposure along the walk to school was significantly greater for children living in deprived catchments, compared with children living in affluent school catchments (t = -5.25, p Conclusion/Implications Increased BMI was associated with greater RFE exposure along the walk home from school. The findings suggest that the walk home from school should be the focus for developing interventions and policies to discourage unhealthy eating. Research should be undertaken to better understand child purchasing habits.

Introduction Green-blue spaces (GBS), such as parks, woodlands, and beaches, may be beneficial for population mental health and wellbeing. However, there are few longitudinal studies on the association between GBS and mental health and wellbeing, and few that incorporate network analysis as opposed to simple Euclidian proximity. Objectives and Approach We are examining the association between the availability of GBS with wellbeing and common mental health disorders. We will use geographic information systems (GIS) to create quarterly household level GBS availability data using digital map and satellite data (2008-2018) for over 1 million homes in Wales, United Kingdom. We will link GBS availability to individual level mental health (1.7 million people with General Practitioner (GP) data) and data from the National Survey for Wales (n = 24,000) on wellbeing (Warwick Edinburgh Mental Wellbeing Scale (WEMWBS)) using the Secure Anonymised Information Linkage (SAIL) databank. Results We created an historic dataset of GBS availability using road network and path data to create quarterly household level GBS exposures (2008-2018). We tested Residential Anonymised Linking Fields (RALFs) and accurately linked 97\% of individuals and their health data to their home and GBS exposure. The 1.65 million exposure-health data pairs, updated quarterly, will enable a longitudinal panel study to be built. Using GP recorded data on treatments, diagnoses, symptoms and prescriptions for mental health problems we identified 35,000 people had a common mental health disorder in 2016, and 24,000 people answered the National Survey for Wales questions about their wellbeing and use of GBS. We will explore how house moves, and visits to GBS change the association between GBS availability and outcomes. Conclusion/Implications This study fills the gap in the evidence base around environmental planning policy to shape living environments to benefit health. It will inform the planning and management of GBS in urban and rural environments and contribute to international work on impacts of the built environment on mental health and wellbeing.

Introduction There is a lack of evidence of the adverse effects of air pollution and pollen on cognition for people with air quality related health conditions. This study explored the effects of air quality and respiratory health conditions on educational attainment for 18,241 pupils across the city of Cardiff, United Kingdom. Objectives and Approach Anonymised, routinely collected health and education data were linked at the household and school level with modelled high spatial resolution pollution data, and daily pollen measurements using the Secure Anonymised Information Linkage (SAIL) databank. This created 7 repeated cross-sectional cohorts (2009-2015). Multilevel linear regression analysis examined whether exam performance was associated with health status and/or air quality levels averaged at school and home locations during revision and examination periods. We also investigated the combined effects of air quality and associations with educational attainment for pupils who were treated for asthma and/or Severe Allergic Rhinitis (SAR), and those who were not. Results The cohort contained 9337 males and 8904 female pupils. There were 871 treated for asthma, 2091 for SAR, and 634 treated for both. Asthma was not associated with exam performance (p=0.700). However, SAR was positively associated with exam performance (p 2) was negatively associated with educational attainment (p = 0.002). Other indicators of air quality (pollutants: Ozone, Particulate Matter - PM2.5, and pollen) were not associated with educational attainment (p> 0.05). Exposure to NO2 was negatively associated with educational attainment irrespective of treatment for asthma or SAR. There was no combined effect of air quality on the variation in educational attainment between those who are treated for asthma and/or SAR and those who were not. Conclusion/Implications Irrespective of health status, exposure to NO2 was negatively associated with educational attainment. Treatment seeking behaviour may be a possible explanation for the positive association between SAR and educational attainment. For a more accurate reflection of health status, health outcomes not subject to treatment seeking behaviour should be investigated.

Introduction The Secure Anonymised Information Linkage (SAIL) databank facilitated linkage of routinely collected health and education data, high spatial resolution pollution modelling and daily pollen measurements for 18,241 pupils in 7 cross-sectional cohorts across Cardiff city, UK, to investigate effects of air quality and respiratory health conditions on education attainment. Objectives and Approach An urban atmospheric dispersion and chemistry modelling system (ADMS-Urban) simulated modelled hourly concentrations of air pollutants: PM2.5, PM10, NO2 and ozone levels. These were summarised into minimum, average and maximum daily readings for 4 time periods (e.g. school hours 9am-3pm) for all home and school locations across Cardiff between 2009 and 2015. The combination of different pollutants, measurements and time-periods created a comprehensive multi-row dataset per location. We transformed the dimensionality of this high-resolution data to create one row of summarised data per pupil per cohort, in preparation for statistical analysis. Results 157,361 school and home locations across Cardiff were anonymised and household linkage fields were appended to combine pollution estimates at the household/school to individual health data. The pollution dataset contained 369 columns, 472,083 rows per year with one column per location, pollutant type, pollutant measurement, daily time-period, and day of year. Dataset transformation reduced algorithm computation by creating a single date column, producing a five column, 3,446,205,900-row matrix per year dataset. The algorithm adjusted for weekends, school/bank holidays and allowed location to vary 3pm-5pm on school days when pupil location was uncertain. The algorithm calculated tailored pollution exposures per pupil for revision and examination periods, creating one row per pupil and reducing 7 years of data and 24 billion rows to 18,241. Conclusion/Implications We successfully linked 95% of the cohorts’ household/school pollution data to their corresponding health and education data. This demonstrates data linking retrospective exposures for total populations using multiple daily locations, and extends our analysis platform for natural experiments to include daily exposure. Future work includes adding modelled route exposures.

Introduction There is a lack of evidence on the adverse effects of air pollution on cognition for people with air quality-related health conditions. We propose that educational attainment, as a proxy for cognition, may increase with improved air quality. This study will explore whether asthma and seasonal allergic rhinitis, when exacerbated by acute exposure to air pollution, is associated with educational attainment. Objectives To describe the preparation of individual and household-level linked environmental and health data for analysis within an anonymised safe haven. Also to introduce our statistical analysis plan for our study: COgnition, Respiratory Tract illness and Effects of eXposure (CORTEX). Methods We imported daily air pollution and aeroallergen data, and individual level education data into the SAIL databank, an anonymised safe haven for person-based records. We linked individual-level education, socioeconomic and health data to air quality data for home and school locations, creating tailored exposures for individuals across a city. We developed daily exposure data for all pupils in repeated cross sectional exam cohorts (2009-2015). Conclusion We have used the SAIL databank, an innovative, data safe haven to create individual-level exposures to air pollution and pollen for multiple daily home and school locations. The analysis platform will allow us to evaluate retrospectively the impact of air quality on attainment for multiple cross-sectional cohorts of pupils. Our methods will allow us to distinguish between the pollution impacts on educational attainment for pupils with and without respiratory health conditions. The results from this study will further our understanding of the effects of air quality and respiratory-related health conditions on cognition. Highlights • This city-wide study includes longitudinal routinely-recorded educational attainment data for all pupils taking exams over seven years; • High spatial resolution air pollution data were linked within a privacy protected databank to obtain individual exposure at multiple daily locations; • This study will use health data linked at the individual level to explore associations between air pollution, related morbidity, and educational attainment.

Background and Objectives There is a lack of evidence of the adverse effects of air pollution and pollen on cognition for people with air quality-related health conditions. The CORTEX project combined routinely collected health and education data, high spatial resolution air pollution modelling, and daily pollen measurements for 18,241 pupils living in Cardiff, UK, between 2009 and 2015, to investigate the acute effects of air quality and respiratory conditions on education attainment. Datasets Air pollutants PM2.5, PM10, NO2, and ozone levels were modelled for 157,361 home and school locations, anonymised into the Secure Anonymised Information Linkage (SAIL) Databank, and summarised into minimum, average and maximum readings for 4 daily time periods reflecting pupil home/school exposure. Adding a unique Residential Anonymised Linking Field (RALF) allowed linkage of pollution estimates to individual level data. Annual pollution datasets contained 369 columns and 472,083-rows, with one column per location, pollutant, daily time-period and day of year. Dataset transformation produced a 5 column, 3,446,205,900-row matrix per year. Methods and Conclusions An algorithm using Structured Query Language (SQL) to manage data held within a relational database management system, was designed to reduce dimensionality from 24 billion to 18,241 rows of data. The algorithm calculated average means for each pollutant (PM2.5, PM10, NO2, and ozone levels) over the revision and examination periods, and summarised data into one row per pupil. The algorithm adjusted for weekends, school, and bank holidays, it calculated daily pollutant exposure for each pupil, and successfully linked 95% of pupil pollution exposures to their health and education data.

Background The dose-response relationship between exposure to food environments and obesity has not been widely investigated. This study examined whether increased retail food environment (RFE) exposure in children was associated with a larger body mass index (BMI). Objectives Generate household level daily exposure to the RFE for children aged 11-13 years and link these environmental exposure with health data in an anonymised data safe haven. Methods Individually tailored environmental exposures were calculated in a GIS for home and school locations, and modelled walking routes to and from school. Local Authority food outlet data were used to generate the temporally accurate exposures. Exposures were linked to individual level health data in the SAIL databank for a cohort of individuals from south Wales aged 11-13 years, with BMI measurements. A fully adjusted multilevel regression model was fitted to investigate the association of RFE exposure with BMI. Findings Home exposure and exposure along the walk to school was significantly greater for children living in deprived catchments, compared with affluent school catchments (t = -5.25, p

Background There is a lack of evidence of the adverse effects which air quality has on cognition for people with air quality-related health conditions, these are not widely documented in the literature. Educational attainment, as a proxy for cognition, may increase with improved air quality. Objectives Prepare individual and household level linked environmental and health data for analysis within an anonymised safe haven; analyse the linked dataset for our study investigating: Cognition, Respiratory Tract illness and Effects of eXposure (CORTEX). Methods Anonymised, routinely collected health and education data were linked with high spatial resolution pollution measurements and daily pollen measurements to provide repeated cross-sectional cohorts (2009-2015) on 18,241 pupils across the city of Cardiff, using the SAIL databank. A fully adjusted multilevel linear regression analysis examined associations between health status and/or air quality. Cohort, school and individual level confounders were controlled for. We hope that using individual-level multi-location daily exposure assessment will help to clarify the role of traffic and prevent potential community-level confounding. Combined effects of air quality on variation in educational attainment between those treated for asthma and/or Severe Allergic Rhinitis (SAR), and those not treated, was also investigated. Findings Asthma was not associated with exam performance (p=0.7). However, SAR was positively associated with exam performance (p

The aim of this study was to quantify the error associated with different accessibility methods commonly used by public health researchers. Network distances were calculated from each household to the nearest GP our study area in the UK. Household level network distances were assigned as the gold standard and compared to alternate widely used accessibility methods. Four spatial aggregation units, two centroid types and two distance calculation methods represent commonly used accessibility calculation methods. Spearman's rank coefficients were calculated to show the extent which distance measurements were correlated with the gold standard. We assessed the proportion of households that were incorrectly assigned to GP for each method. The distance method, level of spatial aggregation and centroid type were compared between urban and rural regions. Urban distances were less varied from the gold standard, with smaller errors, compared to rural regions. For urban regions, Euclidean distances are significantly related to network distances. Network distances assigned a larger proportion of households to the correct GP compared to Euclidean distances, for both urban and rural morphologies. Our results, stratified by urban and rural populations, explain why contradicting results have been reported in the literature. The results we present are intended to be used aide-memoire by public health researchers using geographical aggregated data in accessibility research.

ABSTRACT Objectives Linking routinely collected health and environment data can allow for large scale evaluations of how our environment impacts our health. Our data linkage approach advances previous research where residence-based environmental exposures were anonymously linked in the SAIL databank using Residential Anonymous Linking Fields (RALFs). The dose-response relationship between exposure to food and dietary intake has not been widely investigated. Previous research found conflicting views on whether increased environmental exposure to unhealthy food contributes to higher BMIs. This may have been due to different methodological approaches, including imprecise exposures, small numbers, and the use of self-reported BMIs. Approach This investigation calculated food exposure environments for routes from all homes to and from school. A Geographic Information System was used to calculate the environmental exposures along all potential routes up to a maximum age-appropriate walking distance from each school. Once within the SAIL databank we selected relevant routes using linked demographic and pupil datasets. To maintain privacy, the primary (doctoral) researcher generating the environmental exposures, did not have access to the final household-level exposure data in their identifiable form. The researcher automated their method so a second researcher could run the GIS analysis. Accuracy of modelled exposures will be compared with actual routes collected from GPS traces of children walking to school. Results Removing access to the final identifiable household-level route exposures enabled the primary researcher to complete analysis on the combined household and individual-level data within the secure environment. The environmental exposures were linked with routine health data from the SAIL databank; including BMI as an indicator of obesity. BMI data for 4-5 year olds, and a sample of 1300 13-14 year olds were linked to associated environmental exposures. Conclusion Depending on modelled accuracy, a GIS and data linkage approach may allow the investigation of natural experiments and intervention evaluation at the scale of the total population. This is the first step towards anonymously modelling part of the daily exposure environment using routine data. A limitation is the lack of routinely collected BMI data for older children and teenagers an age when they are more likely to have the option to choose to buy food on the school route. This work will have many potential applications, including the delivery and evaluation of multiple school and workplace commuting interventions.