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.

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.

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.

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.

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 center dot 1 increased EVI on a 0-1 scale) was associated with lower odds of subsequent CMD (adjusted odds ratio 0 center dot 80, 95% CI 0 center dot 80-0 center dot 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 center dot 93, 0 center dot 93-0 center dot 93). Every additional 360 m to the nearest GBS was associated with higher odds of CMD (1 center dot 05, 1 center dot 04-1 center dot 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.

Introduction School-based COVID-19 mitigation strategies have greatly impacted the primary school day (children aged 3–11) including: wearing face coverings, two metre distancing, no mixing of children, and no breakfast clubs or extra-curricular activities. This study examines these mitigation measures and association with COVID-19 infection, respiratory infection, and school staff wellbeing between October to December 2020 in Wales, UK. Methods A school staff survey captured self-reported COVID-19 mitigation measures in the school, participant anxiety and depression, and open-text responses regarding experiences of teaching and implementing measures. These survey responses were linked to national-scale COVID-19 test results data to examine association of measures in the school and the likelihood of a positive (staff or pupil) COVID-19 case in the school (clustered by school, adjusted for school size and free school meals using logistic regression). Linkage was conducted through the SAIL (Secure Anonymised Information Linkage) Databank. Results Responses were obtained from 353 participants from 59 primary schools within 15 of 22 local authorities. Having more direct non-household contacts was associated with a higher likelihood of COVID-19 positive case in the school (1–5 contacts compared to none, OR 2.89 (1.01, 8.31)) and a trend to more self-reported cold symptoms. Staff face covering was not associated with a lower odds of school COVID-19 cases (mask vs. no covering OR 2.82 (1.11, 7.14)) and was associated with higher self-reported cold symptoms. School staff reported the impacts of wearing face coverings on teaching, including having to stand closer to pupils and raise their voices to be heard. 67.1% were not able to implement two metre social distancing from pupils. We did not find evidence that maintaining a two metre distance was associated with lower rates of COVID-19 in the school. Conclusions Implementing, adhering to and evaluating COVID-19 mitigation guidelines is challenging in primary school settings. Our findings suggest that reducing non-household direct contacts lowers infection rates. There was no evidence that face coverings, two metre social distancing or stopping children mixing was associated with lower odds of COVID-19 or cold infection rates in the school. Primary school staff found teaching challenging during COVID-19 restrictions, especially for younger learners and those with additional learning needs.

Cohort Profile: The Green and Blue Spaces (GBS) and mental health in Wales e-cohort Daniel A Thompson , Rebecca S Geary, Francis M Rowney, Richard Fry , Alan Watkins, Benedict W Wheeler, Amy Mizen, Ashley Akbari, Ronan A Lyons, Gareth Stratton, James White and Sarah E Rodgers* Population Data Science, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea UK, Department of Public Health, Policy and Systems, University of Liverpool, Liverpool, UK, European Centre for Environment and Human Health, University of Exeter Medical School, Knowledge Spa, Royal Cornwall Hospital, Cornwall, UK, Department of Sport and Exercise Sciences, Applied Sports Technology, Exercise and Medicine A-STEM Research Centre, School of Engineering and Applied Sciences, Faculty of Science and Engineering, Swansea University, Swansea UK and Centre for Trials Research, School of Medicine, Cardiff University, Cardiff, UK

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.

BackgroundGrowing cross-sectional evidence links access to green-blue spaces with mental health benefits, but studies at an individual level and at a national population scale are scarce. This gap can be addressed through the Secure Anonymised Information Linkage (SAIL) Databank, which allows household-level green-blue spaces access and exposure data to be linked to individual-level health-care use.MethodsWithin the SAIL Databank, an e-cohort of the population of Wales (2008–19) was created from green-blue space metrics and the Welsh Longitudinal General Practice database. Green-blue spaces metrics (derived from satellite imagery and planning data) included average ambient greenness within 300 m of the home (designated as the Enhanced Vegetation Index) and average access to green-blue spaces (designated as the number of green-blue spaces within 1600 m of the home). A validated algorithm was applied to create a common mental health disorder flag and linked to green-blue spaces exposure (ambient greenness and access) recorded for individuals not affected by common mental health disorders. We used multivariate logistic regression models to test the hypothesis that greater green-blue spaces exposure is associated with a reduced likelihood of a flagged common mental health disorder. Subgroup analyses were done for socioeconomic deprivation.FindingsThe e-cohort comprised 2 341 591 individuals (1 193 240 men and 1 148 351 women), aged 16 or over and registered with a general practice in the SAIL Databank. After adjusting for individual and area-level covariates, a unit increase in ambient greenness around the home and access to green-blue spaces within 1600 m were associated with lower odds of a common mental health disorder (adjusted odds ratio 0·11 [95% CI 0·11–0·12] for ambient greenness around the home and 0·47 [0·46–0·48] for access to green-blue spaces within 1600 m). A unit increase in ambient greenness was associated with reduced odds of a common mental health disorder for residents of the most deprived areas (n=473 410; 0·22 [0·20–0·24]) and of the least deprived areas (n=480 424; 0·07 [0·07–0·08]).InterpretationPeople with greater exposure to green-blue spaces were less likely to develop a common mental health disorder and the effect is modified by socioeconomic deprivation. This finding has implications for both public health policy and urban planning. This large, adult-population cohort provides sufficient power to examine variations between subgroups to investigate inequalities.FundingThe project was developed as part of independent research funded by the National Institute for Health Research (study number 16/07/07).

BackgroundPhysical housing and household composition have an important role in the lives of individuals and drive health and social outcomes, and inequalities. Most methods to understand housing composition are based on survey or census data, and there is currently no reproducible methodology for creating population-level household composition measures using linked administrative data.MethodsUsing existing, and more recent enhancements to the address-data linkage methods in the SAIL Databank using Residential Anonymised Linking Fields we linked individuals to properties using the anonymised Welsh Demographic Service data in the SAIL Databank. We defined households, household size, and household composition measures based on adult to child relationships, and age differences between residents to create relative age measures.ResultsTwo relative age-based algorithms were developed and returned similar results when applied to population and household-level data, describing household composition for 3.1 million individuals within 1.2 million households in Wales. Developed methods describe binary, and count level generational household composition measures.ConclusionsImproved residential anonymised linkage field methods in SAIL have led to improved property-level data linkage, allowing the design and application of household composition measures that assign individuals to shared residences and allow the description of household composition across Wales. The reproducible methods create longitudinal, household-level composition measures at a population-level using linked administrative data. Such measures are important to help understand more detail about an individual’s home and area environment and how that may affect the health and wellbeing of the individual, other residents, and potentially into the wider community.

BackgroundEvidence that greenspaces are related to mental health and wellbeing mostly relies on residential exposure and few studies have considered actual use. We aimed to link the National Survey for Wales (NSW) to environmental metrics to determine whether there is an association between increased residential exposure to greenspace and subjective wellbeing, and whether this is mediated by visits to outdoor spaces.MethodsIn this cross-sectional data linkage study, we linked NSW data (2016–17 and 2018–19; repeat cross-sectional) to the Secure Anonymised Information Linkage (SAIL) Databank. Survey data included the Warwick and Edinburgh Mental Wellbeing Scale (WEMWBS), and self-reported time spent on leisure visits to open spaces in Wales (including bluespaces; derived from visit frequency in the past 4 weeks and duration of the main activity on the most recent visit). Linkage at individual level augmented this data with home neighbourhood greenspace data (Enhanced Vegetation Index [EVI] derived from satellite imagery). Using multivariate linear regression models, we estimated associations between home or visit exposures and WEMWBS, adjusting for various covariates including area level (via Welsh Index of Multiple Deprivation) and individual deprivation indicators. EVI, weekly time outdoors, and WEMWBS were standardised and both linear and quadratic terms were included in analyses. The e-cohort uses anonymised data and was approved by the SAIL Information Governance Review Panel.FindingsAmong NSW respondents providing outcome measures (n=5971), EVI was significantly related to both WEMWBS (U-shaped relationship, EVI beta −0·02, p=0·13; EVI2 beta 0·02, p= 0·0098), and weekly time outdoors (EVI beta 0·04, p=0·020). Time outdoors was also significantly related to WEMWBS (time outdoors beta 0·17, p<0·0001; time outdoors2 beta –0·04, p=0·018). Despite the conditions for potential mediation being met—i.e., EVI predicting both time outdoors and WEMWBS and time outdoors predicting WEMWBS—there was no evidence that time outdoors mediated the relationship between EVI and WEMWBS. EVI coefficients were not attenuated after including time outdoors (EVI beta –0·03, p=0·076; EVI2 beta 0·02, p=0·018).InterpretationLiving near and time spent visiting greenspaces and bluespaces were both related to better subjective wellbeing. The absence of mediation suggests that better wellbeing associated with EVI occurs through a different mechanism than visiting.FundingNational Institute for Health Research.

Background Better understanding of the role that children and school staff play in the transmission of SARS-CoV-2 is essential to guide policy development on controlling infection while minimising disruption to children’s education and well-being.Methods Our national e-cohort (n=464531) study used anonymised linked data for pupils, staff and associated households linked via educational settings in Wales. We estimated the odds of testing positive for SARS-CoV-2 infection for staff and pupils over the period August– December 2020, dependent on measures of recent exposure to known cases linked to their educational settings.Results The total number of cases in a school was not associated with a subsequent increase in the odds of testing positive (staff OR per case: 0.92, 95% CI 0.85 to 1.00; pupil OR per case: 0.98, 95% CI 0.93 to 1.02). Among pupils, the number of recent cases within the same year group was significantly associated with subsequent increased odds of testing positive (OR per case: 1.12, 95% CI 1.08 to 1.15). These effects were adjusted for a range of demographic covariates, and in particular any known cases within the same household, which had the strongest association with testing positive (staff OR: 39.86, 95% CI 35.01 to 45.38; pupil OR: 9.39, 95% CI 8.94 to 9.88).Conclusions In a national school cohort, the odds of staff testing positive for SARS-CoV-2 infection were not significantly increased in the 14-day period after case detection in the school. However, pupils were found to be at increased odds, following cases appearing within their own year group, where most of their contacts occur. Strong mitigation measures over the whole of the study period may have reduced wider spread within the school environment.

Introduction The COVID-19 pandemic has highlighted the need for robust data linkage systems and methods for identifying outbreaks of disease in near real-time. Objectives The primary objective of this study was to develop a real-time geospatial surveillance system to monitor the spread of COVID-19 across the UK. Methods Using self-reported app data and the Secure Anonymised Information Linkage (SAIL) Databank, we demonstrate the use of sophisticated spatial modelling for near-real-time prediction of COVID-19 prevalence at small-area resolution to inform strategic government policy areas. Results We demonstrate that using a combination of crowd-sourced app data and sophisticated geo-statistical techniques it is possible to predict hot spots of COVID-19 at fine geographic scales, nationally. We are also able to produce estimates of their precision, which is an important pre-requisite to an effective control strategy to guard against over-reaction to potentially spurious features of ’best guess’ predictions. Conclusion In the UK, important emerging risk-factors such as social deprivation or ethnicity vary over small distances, hence risk needs to be modelled at fine spatial resolution to avoid aggregation bias. We demonstrate that existing geospatial statistical methods originally developed for global health applications are well-suited to this task and can be used in an anonymised databank environment, thus preserving the privacy of the individuals who contribute their data.

IntroductionCommon mental health disorders (CMD) are significant contributors to impaired health and well-being, and drive greater health resource utilisation. Electronic health records (EHR) are increasingly used for case identification of CMD when ascertaining social determinants of mental health. We seek to compare self-reported well-being indicators in groups identified using EHR-based CMD methods. Objectives and ApproachThe National Survey for Wales (NSW) contains self-reported well-being indicators (Warwick Edinburgh Mental Well-being Scale, WEMWBS) recorded annually on ~7,000 individuals. We combined data from two NSWs and linked well-being indicators with Welsh Longitudinal General Practice (WLGP) data within the Secure Anonymised Information Linkage (SAIL) Databank, using individual response dates. We then used WGLP data to algorithmically derive identifiers of CMD cases within survey respondents. This individual-level linkage enables a comparison of NSW responses in CMD and non-CMD cases, and to assess sensitivity and specificity of the current CMD algorithm. ResultsSurvey participants comprised 18,450 adults aged 16+ and living in Wales during 16/17 or 18/19. WEMWBS responses indicate 2,338 (12.6%) participants could be considered possibly depressed, and 2,268 (12.3%) probably depressed with low mental well-being (LMW). For participants with LMW, a 42/58 percentage split is observed between male/female respondents, compared to a 45/55 respective split of those not identified with LMW. Participants with LMW recorded low measures for overall satisfaction with life, 998 (44%) reported a value of 5 or less (/10) compared to 1123 (7%) participants not identified with LMW. Similarly, 828 (37%) participants identified with LMW reported 5 or less (/10) on the life worthwhile index, compared to 800 (5%) of non-LMW participants. Conclusion / ImplicationsLinkage to the NSW provides a rich data source to compare objective well-being to algorithmically derived CMD cases from routinely collected primary care data. The individual-level linkage involved will allow for the wider determinants of mental health disorders to be examined.

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.

Abstract. This paper presents an analysis of wave recordings with particular attention to assessing bimodality of the incident wave energy spectra and the occurrence of swell along the south and south-west coasts of the United Kingdom, (UK). A procedure is developed to perform an intensive analysis of a new and large dataset of measured wave spectra. A storm during February 2014 is analysed in detail, highlighting the observed wave conditions leading up to and during the collapse of the sea wall at Dawlish, UK. The analysis reveals the prevalence of trapped-fetch conditions and long-period swell during the February 2014 storm. Bimodality and the presence of swell are compared at three locations along the south coast of the UK. Results highlight the increase in bimodality during the 2013/2014 storm period, especially at Dawlish. The analysis also provides evidence of bimodality and swell waves occurring far along the English Channel. Observed wave conditions at Dawlish are compared to the parametric limits of empirical formulae to estimate wave overtopping. There were numerous instances of peak wave periods or wave heights outside the limits of the formulae, showing that existing design formulae do not yet adequately account for the range of conditions experienced in coastal waters.

The work presents a methodology to assess the coastal impacts during a storm event which caused significant damage along the promenade at Aberystwyth, Wales on the 3 January 2014. Overtopping was analysed in detail for a section of promenade by downscaling offshore wave conditions to force a surf zone hydrodynamic model, NEWRANS. Overtopping discharges are computed and were in qualitative agreement with published discharges for the level of damage observed along the promenade. Peak storm conditions were observed to arrive just before and during high tide at Aberystwyth, which in addition to a storm surge and wave-setup, contributed to the damage observed. A high frequency of overtopping occurs during peak high tide, with overtopping also occurring in the hour leading up to and following high tide. Finally, comparisons to design methods for the estimation of overtopping discharge were made. Current empirical formulae underestimated the peak overtopping event at high tide. The methodology applied is generic and applicable to any location.

This paper describes an investigation of the generation of desired sea states in a numerical wave model. Bimodal sea states containing energetic swell components can be coastal hazards along coastlines exposed to large oceanic fetches. Investigating the effects of long-period bimodal seas requires large computational domains and increased running time to ensure the development of the desired sea state. Long computational runs can cause mass stability issues due to the Stokes drift and wave reflection, which in turn affect results through the variation of the water level. A numerical wave flume, NEWRANS, was used to investigate two wave generation methods: the wave paddle method, allowing for a smaller domain; and the internal mass source function method, providing an open boundary allowing reflected waves to leave the domain. The two wave generation methods were validated against experimental data by comparing the wave generation accuracy and the variance of mass in the model during simulations. Results show that the wave paddle method not only accurately generates the desired sea state but also provides a more stable simulation, in which mass fluctuation has less of an effect on the water depth during the long-duration simulations. As a result, it is suggested that the wave paddle method with active wave absorption is preferable to the internal wave maker option when investigating intermediate-depth long-period bimodal seas for long-duration simulations.

Introduction The COVID-19 pandemic has highlighted the need for robust data linkage systems and methods for identifying outbreaks of disease in near real-time. Objectives The primary objective of this study was to develop a real-time geospatial surveillance system to monitor the spread of COVID-19 across the UK. Methods Using self-reported app data and the Secure Anonymised Information Linkage (SAIL) Databank, we demonstrate the use of sophisticated spatial modelling for near-real-time prediction of COVID-19 prevalence at small-area resolution to inform strategic government policy areas. Results We demonstrate that using a combination of crowd-sourced app data and sophisticated geo-statistical techniques it is possible to predict hot spots of COVID-19 at fine geographic scales, nationally. We are also able to produce estimates of their precision, which is an important pre-requisite to an effective control strategy to guard against over-reaction to potentially spurious features of ’ best guess ’ predictions. Conclusion In the UK, important emerging risk-factors such as social deprivation or ethnicity vary over small distances, hence risk needs to be modelled at fine spatial resolution to avoid aggregation bias. We demonstrate that existing geospatial statistical methods originally developed for global health applications are well-suited to this task and can be used in an anonymised databank environment, thus preserving the privacy of the individuals who contribute their data.