Children and young people's body mass index measures derived from routine data sources: A national data linkage study in Wales

BACKGROUND

Routine monitoring of Body Mass Index (BMI) in general practice, and via national surveillance programmes, is essential for the identification, prevention, and management of unhealthy childhood weight. We examined and compared the presence and representativeness of children and young people's (CYPs) BMI recorded in two routinely collected administrative datasets: general practice electronic health records (GP-BMI) and the Child Measurement Programme for Wales (CMP-BMI), which measures height and weight in 4-5-year-old school children. We also assessed the feasibility of combining GP-BMI and CMP-BMI data for longitudinal analyses.

METHODS

We accessed de-identified population-level GP-BMI data for calendar years 2011 to 2019 for 246,817 CYP, and CMP-BMI measures for 222,772 CYP, held within the Secure Anonymised Information Linkage Databank. We examined the proportion of CYP in Wales with at least one GP-BMI record, its distribution by child socio-demographic characteristics, and trends over time. We compared GP-BMI and CMP-BMI distributions. We quantified the proportion of children with a CMP-BMI measure and a follow-up GP-BMI recorded at an older age and explored the representativeness of these measures.

RESULTS

We identified a GP-BMI record in 246,817 (41%) CYP, present in a higher proportion of females (54.2%), infants (20.7%) and adolescents. There was no difference in the deprivation profile of those with a GP-BMI measurement. 31,521 CYP with a CMP-BMI had at least one follow-up GP-BMI; those with a CMP-BMI considered underweight or very overweight were 87% and 70% more likely to have at least one follow-up GP-BMI record respectively compared to those with a healthy weight, as were males and CYP living in the most deprived areas of Wales.

CONCLUSIONS

Records of childhood weight status extracted from general practice are not representative of the population and are biased with respect to weight status. Linkage of information from the national programme to GP records has the potential to enhance discussions around healthy weight at the point of care but does not provide a representative estimate of population level weight trajectories, essential to provide insights into factors determining a healthy weight gain across the early life course. A second CMP measurement is required in Wales.

The use of Enhanced Vegetation Index for assessing access to different types of green space in epidemiological studies

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 m2, 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 m2. 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.

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

BACKGROUND

Reducing the burden of falls and fall-related admissions to hospital and care homes is an important policy area because falls cause significant injury leading to a reduced quality of life. We investigated the effect of the environment around people's homes on the risk of falls for older people in Wales.

METHODS

In this longitudinal cohort study, we created a dynamic national e-cohort of individuals aged 60 years or older living in Wales between Jan 1, 2010, and Dec 31, 2019. Using the Secure Anonymised Information Linkage Databank, we linked routinely collected, anonymised health-data on general practitioner (GP) appointments; hospital and emergency admissions; and longitudinal individual-level demographic data to metrics detailing the built environment and deprivation as determined by the Welsh Index of Multiple Deprivation. Using adjusted cox regression models, we assessed how the risk of a fall changed with sex, age, deprivation quintile, urban or rural classification, household occupancy, care status, frailty, dementia diagnosis, and built environment metrics. Built environments of urban and rural areas are very different, so we stratified our analysis by urbanicity to compare these associations in each setting.

FINDINGS

We analysed 5 536 444 person-years of data from 931 830 individuals (sex: 51·5% female, 48·5% male; age: 69·2% aged 60-64 years, 12·3% aged 65-69 years, 13·3% aged 70-79 years, 4·4% aged 80-89 years, and 0·7% aged ≥90 years). 154 060 (16·5%) had a fall between joining the cohort and Dec 31, 2019. Men had a lower risk of falling than women (adjusted hazard ratio [aHR] 0·736 [0·729-0·742]), and the risk increased with age compared with individuals aged 60-64 years (1·395 [1·378-1·412] for 65-69 years, 1·892 [1·871-1·913] for 70-79 years, 2·668 [2·623-2·713] for 80-89 years, 3·196 [3·063-3·335] for ≥90 years) and with frailty compared with fit individuals (1·609 [1·593-1·624] for mild frailty, 2·263 [2·234-2·293] for moderate frailty, and 2·833 [2·770-2·897] for severe frailty). Those living in rural areas were less likely to fall than those in urban areas (0·711 [0·702-0·720]). All p values were less than 0·0001.

INTERPRETATION

Although preliminary, these results corroborate current knowledge that as we age and become frailer, the risk of falling increases. The effect of urbanicity on risk of fall suggests that the built environment could be associated with fall risk. We only detected falls that caused emergency or hospital admission, leading to potential selection bias. Nevertheless, this research could help guide policy to reduce the incidence of injuries caused by falls in older people.

FUNDING

Health and Care Research Wales.

Impact of individual, household, and area characteristics on health and social care outcomes for people with multimorbidity: Protocol for a multilevel analysis

BACKGROUND

Multimorbidity 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.

METHODS

The 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.

DISCUSSION

Household 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.

Green-blue space exposure changes and impact on individual-level well-being and mental health: a population-wide dynamic longitudinal panel study with linked survey data

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.

Ambient greenness, access to local green spaces, and subsequent mental health: a 10-year longitudinal dynamic panel study of 2·3 million adults in Wales

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.

Impact of data source choice on multimorbidity measurement: a comparison study of 2.3 million individuals in the Welsh National Health Service

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 < 0.20), fair for 12 (kappa 0.21-0.40), moderate for 16 (kappa 0.41-0.60), and substantial for 12 (kappa 0.61-0.80) conditions, and by body system was lowest for mental and behavioural disorders. The percentage agreement, individuals with a condition identified in both PC and HI data, was lowest in anxiety (4.6%) and highest in coronary artery disease (62.9%).

CONCLUSIONS

The use of single data sources may underestimate prevalence when measuring multimorbidity and many important conditions (especially mental and behavioural disorders). Caution should be used when interpreting findings of research examining individual and multiple long-term conditions using single data sources. Where available, researchers using electronic health data should link primary care and hospital inpatient data to generate more robust evidence to support evidence-based healthcare planning decisions for people with multimorbidity.

Visiting nature is associated with lower socioeconomic inequalities in well-being in Wales

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.

Built Environments And Child Health in WalEs and AuStralia (BEACHES): a study protocol

INTRODUCTION

Childhood obesity and physical inactivity are two of the most significant modifiable risk factors for the prevention of non-communicable diseases (NCDs). Yet, a third of children in Wales and Australia are overweight or obese, and only 20% of UK and Australian children are sufficiently active. The purpose of the Built Environments And Child Health in WalEs and AuStralia (BEACHES) study is to identify and understand how complex and interacting factors in the built environment influence modifiable risk factors for NCDs across childhood.

METHODS AND ANALYSIS

This is an observational study using data from five established cohorts from Wales and Australia: (1) Wales Electronic Cohort for Children; (2) Millennium Cohort Study; (3) PLAY Spaces and Environments for Children's Physical Activity study; (4) The ORIGINS Project; and (5) Growing Up in Australia: the Longitudinal Study of Australian Children. The study will incorporate a comprehensive suite of longitudinal quantitative data (surveys, anthropometry, accelerometry, and Geographic Information Systems data) to understand how the built environment influences children's modifiable risk factors for NCDs (body mass index, physical activity, sedentary behaviour and diet).

ETHICS AND DISSEMINATION

This study has received the following approvals: University of Western Australia Human Research Ethics Committee (2020/ET000353), Ramsay Human Research Ethics Committee (under review) and Swansea University Information Governance Review Panel (Project ID: 1001). Findings will be reported to the following: (1) funding bodies, research institutes and hospitals supporting the BEACHES project; (2) parents and children; (3) school management teams; (4) existing and new industry partner networks; (5) federal, state and local governments to inform policy; as well as (6) presented at local, national and international conferences; and (7) disseminated by peer-reviewed publications.

Multi-sectoral data linkages to explore associations between the built environment and BMI

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

Associations between viewable greenspace and adolescent wellbeing in Wales

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

COVID-19 risk factors amongst 14,786 care home residents: an observational longitudinal analysis including daily community positive test rates of COVID-19, hospital stays and vaccination status in Wales (UK) between 1 September 2020 and 1 May 2021

BACKGROUND

COVID-19 vaccinations have been prioritised for high risk individuals.

AIM

Determine individual-level risk factors for care home residents testing positive for SARS-CoV-2.

STUDY DESIGN

Longitudinal observational cohort study using individual-level linked data from the Secure Anonymised Information Linkage (SAIL) databank.

SETTING

Fourteen thousand seven hundred and eighty-six older care home residents (aged 65+) living in Wales between 1 September 2020 and 1 May 2021. Our dataset consisted of 2,613,341 individual-level daily observations within 697 care homes.

METHODS

We estimated odds ratios (ORs [95% confidence interval]) using multilevel logistic regression models. Our outcome of interest was a positive SARS-CoV-2 PCR test. We included time-dependent covariates for the estimated community positive test rate of COVID-19, hospital inpatient status, vaccination status and frailty. Additional covariates were included for age, sex and specialist care home services.

RESULTS

The multivariable regression model indicated an increase in age (OR 1.01 [1.00,1.01] per year), community positive test rate (OR 1.13 [1.12,1.13] per percent increase), hospital inpatients (OR 7.40 [6.54,8.36]), and residents in care homes with non-specialist dementia care (OR 1.42 [1.01,1.99]) had an increased odds of a positive test. Having a positive test prior to the observation period (OR 0.58 [0.49,0.68]) and either one or two doses of a vaccine (0.21 [0.17,0.25] and 0.05 [0.02,0.09], respectively) were associated with a decreased odds.

CONCLUSIONS

Care providers need to remain vigilant despite the vaccination rollout, and extra precautions should be taken when caring for the most vulnerable. Minimising potential COVID-19 infection for care home residents when admitted to hospital should be prioritised.

Green-Blue Spaces and Mental Health: A Longitudinal Data Linkage Study

IntroductionA 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 ApproachWe 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. ResultsWe 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 / ImplicationsThis example of multi-sectoral data linkage across multiple environmental and administrative data sources has created a rich data source, which we will use toquantify 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.

Is Wellbeing Associated with Time Spent in Nature?

IntroductionGreen 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 ApproachOur overall approach will evaluate if residential proximity to GBS is associated with mental health and wellbeing, and if any associations aremodified 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. ResultsUsing 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. ConclusionA 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.

Understanding and responding to COVID-19 in Wales: protocol for a privacy-protecting data platform for enhanced epidemiology and evaluation of interventions

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.

Impact of air pollution on educational attainment for respiratory health treated students: A cross sectional data linkage study

INTRODUCTION

There is some evidence that exam results are worse when students are acutely exposed to air pollution. Studies investigating the association between air pollution and academic attainment have been constrained by small sample sizes.

METHODS

Cross sectional educational attainment data (2009-2015) from students aged 15-16 years in Cardiff, Wales were linked to primary health care data, modelled air pollution and measured pollen data, and analysed using multilevel linear regression models. Annual cohort, school and individual level confounders were adjusted for in single and multi-pollutant/pollen models. We stratified by treatment of asthma and/or Seasonal Allergic Rhinitis (SAR).

RESULTS

A unit (10μg/m3) increase of short-term exposure to NO2 was associated with 0.044 (95% CI: -0.079, -0.008) reduction of standardised Capped Point Score (CPS) after adjusting for individual and household risk factors for 18,241 students. This association remained statistically significant after controlling for other pollutants and pollen. There was no association of PM2.5, O3, or Pollen with standardised CPS remaining after adjustment. We found no evidence that treatment for asthma or SAR modified the observed NO2 effect on educational attainment.

CONCLUSION

Our study showed that short-term exposure to traffic-related air pollution, specifically NO2, was associated with detrimental educational attainment for students aged 15-16. Longitudinal investigations in different settings are required to confirm this possible impact and further work may uncover the long-term economic implications, and degree to which impacts are cumulative and permanent.

GIS-modelled built-environment exposures reflecting daily mobility for applications in child health research

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.

Co-producing a typology for Green and Blue spaces for a longitudinal, national dataset of Green and Blue spaces

Background with rationaleSpending 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 AimQuantify 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. MethodsWe 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. ConclusionWe 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.

GRAPHITE: Geographic Information UK Secure E-Research Platform

BackgroundThe 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 AimIn this case study, we describe our approach to overcoming some of these problems in conjunction with the Secure Anonymised Information Linkage Databank (SAIL). ApproachThe 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. ConclusionSpatial 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.

Longitudinal access and exposure to green-blue spaces and individual-level mental health and well-being: protocol for a longitudinal, population-wide record-linked natural experiment

INTRODUCTION

Studies suggest that access and exposure to green-blue spaces (GBS) have beneficial impacts on mental health. However, the evidence base is limited with respect to longitudinal studies. The main aim of this longitudinal, population-wide, record-linked natural experiment, is to model the daily lived experience by linking GBS accessibility indices, residential GBS exposure and health data; to enable quantification of the impact of GBS on well-being and common mental health disorders, for a national population.

METHODS AND ANALYSIS

This research will estimate the impact of neighbourhood GBS access, GBS exposure and visits to GBS on the risk of common mental health conditions and the opportunity for promoting subjective well-being (SWB); both key priorities for public health. We will use a Geographic Information System (GIS) to create quarterly household GBS accessibility indices and GBS exposure using digital map and satellite data for 1.4 million homes in Wales, UK (2008-2018). We will link the GBS accessibility indices and GBS exposures to individual-level mental health outcomes for 1.7 million people with general practitioner (GP) data and data from the National Survey for Wales (n=~12 000) on well-being in the Secure Anonymised Information Linkage (SAIL) Databank. We will examine if these associations are modified by multiple sociophysical variables, migration and socioeconomic disadvantage. Subgroup analyses will examine associations by different types of GBS. This longitudinal study will be augmented by cross-sectional research using survey data on self-reported visits to GBS and SWB.

ETHICS AND DISSEMINATION

All data will be anonymised and linked within the privacy protecting SAIL Databank. We will be using anonymised data and therefore we are exempt from National Research Ethics Committee (NREC). An Information Governance Review Panel (IGRP) application (Project ID: 0562) to link these data has been approved.The research programme will be undertaken in close collaboration with public/patient involvement groups. A multistrategy programme of dissemination is planned with the academic community, policy-makers, practitioners and the public.

Study protocol for investigating the impact of community home modification services on hospital utilisation for fall injuries: a controlled longitudinal study using data linkage

INTRODUCTION

This study will evaluate the effectiveness of home adaptations, both in preventing hospital admissions due to falls for older people, and improving timely discharge. Results will provide evidence for services at the interface between health and social care, informing policies seeking to promote healthy ageing through prudent healthcare and fall prevention.

METHODS AND ANALYSIS

All individuals living in Wales, UK, aged 60 years and over, will be included in the study using anonymised linked data from the Secure Anonymised Information Linkage Databank. We will use a national database of home modifications implemented by the charity organisation Care & Repair Cymru (C&R) from 2009 to 2017 to define an intervention cohort. We will use the electronic Frailty Index to assign individual levels of frailty (fit, mild, moderate or severe) and use these to create a comparator group (non-C&R) of people who have not received a C&R intervention. Coprimary outcomes will be quarterly numbers of emergency hospital admissions attributed to falls at home, and the associated length of stay. Secondary outcomes include the time in moving to a care home following a fall, and the indicative financial costs of care for individuals who had a fall. We will use appropriate multilevel generalised linear models to analyse the number of hospital admissions related to falls. We will use Cox proportional hazard models to compare the length of stay for fall-related hospital admissions and the time in moving to a care home between the C&R and non-C&R cohorts. We will assess the impact per frailty group, correct for population migration and adjust for confounding variables. Indicative costs will be calculated using financial codes for individual-level hospital stays. Results will provide evidence for services at the interface between health and social care, informing policies seeking to promote healthy ageing through prudent healthcare and prevention.

ETHICS AND DISSEMINATION

Information governance requirements for the use of record-linked data have been approved and only anonymised data will be used in our analysis. Our results will be submitted for publication in peer-reviewed journals. We will also work with lay members and the knowledge transfer team at Swansea University to create communication and dissemination materials on key findings.

Is educational attainment associated with acute exposure to air pollution and pollen, and is it worse for pupils with asthma and seasonal allergic rhinitis?

IntroductionThere 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 ApproachAnonymised, 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. ResultsThe 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/ImplicationsIrrespective 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.

Linking household level GIS-generated environmental exposure scores with individual level anonymised health data

IntroductionThe 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 ApproachHousehold 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”. ResultsA 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 <0.05). Conclusion/ImplicationsWe generated novel regional exposures to combine with historical anonymised health data. Household and individual level linkage of environmental data to health cohorts contributed to the literature to help develop beneficial societal policies. We recommend routine national collections of height and weight for children to allow longitudinal retrospective analyses.

Linking environment and health data to investigate the association between access to unhealthy food and child BMI

IntroductionModelling 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 ApproachIndividually 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). ResultsHome 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/ImplicationsIncreased 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.

Association between blue and green space availability with mental health and wellbeing

IntroductionGreen-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 ApproachWe 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. ResultsWe 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/ImplicationsThis 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.

Cognitive development Respiratory Tract Illness and Effects of eXposure (CORTEX) project: Combining high spatial resolution pollution measurements with individual level data, a methodological approach

IntroductionThe 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 ApproachAn 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. Results157,361 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/ImplicationsWe 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.

Creating individual level air pollution exposures in an anonymised data safe haven: a platform for evaluating impact on educational attainment

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.

Objective

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

Cognitive development Respiratory Tract Illness and Effects of eXposure (CORTEX) project: Data processing challenges in combining high spatial resolution pollution level data with individual level health and education data

Background and ObjectivesThere 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. DatasetsAir 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 ConclusionsAn 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.

Daily exposure to the retail food environment and the association with child BMI

BackgroundThe 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). ObjectivesGenerate 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. MethodsIndividually 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. FindingsHome 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<0.05; t = -0.277, p<0.05, respectively). The RFE exposure along the walk home was the only environmental exposure positively associated with a higher BMI (0.22, p<0.05). ConclusionsIncreased BMI was associated with greater REF exposure along the walk home from school. The findings suggest that the walk home from school may be important for developing interventions and policies to discourage unhealthy eating. Research should be undertaken to better understand child purchasing habits.

Are children who are treated for asthma and seasonal allergic rhinitis disadvantaged in their educational attainment when acutely exposed to air pollution and pollen? A feasibility study

BackgroundThere 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. ObjectivesPrepare 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). MethodsAnonymised, 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. FindingsAsthma was not associated with exam performance (p=0.7). However, SAR was positively associated with exam performance (p<0.001). Exposure to air pollution was negatively associated with educational attainment regardless of health status. ConclusionsIrrespective of health status, air quality was negatively associated with educational attainment. Treatment seeking behaviour may explain the positive association between SAR and educational attainment. For a more accurate reflection of health status, health outcomes not subject to treatment seeking behaviours, such as emergency hospital admission, should be investigated.

Quantifying the Error Associated with Alternative GIS-based Techniques to Measure Access to Health Care Services

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.