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.
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.
IntroductionDemographic profiling is an important aspect of anonymised healthcare research to identify the population of interest. Typically, administrative data is used in conjunction with patient ...registers to create cohorts, but it can be a time consuming process. We describe a method using routinely collected health data to identify vulnerable populations.
Objectives and ApproachUsing existing longitudinal data and the Residential Anonymised Linking Field (RALF) we aim to identify institutions linked to vulnerable populations. We search for specific characteristics of these institutions including the age of occupants, number of current residents, and rate of change of occupants. We also aim to compare our method to a pseudonymised national registry for care homes to ensure it is accurate. This can effectively reduce the need for repeat pseudonymisation of institutions, which is both expensive and time consuming.
ResultsTo implement our method we found the most recent address for living individuals aged 65-95. This produced 202,640 residences from 1,330,335. Of the 202,640 residences, 1347 had four or more cohabitants aged 65-95, and 172 had exactly three residents with ten or more distinct individuals registered over a 10-year period. Our final synthetic dataset therefore had 1519 unique potential care homes to compare to the national registry, which contains 1525 registered care homes.
We can now link the synthetic dataset to individuals to flag their residential status, which may be a defining factor in their level of care. Furthermore, we can answer specific research questions relating to their residency, such as the time it takes to move to a care home following a hospital admission.
Conclusion/ImplicationsBy using quantifiable characteristics of care homes we were able to create a synthetic care home register by searching existing data. This is a reproducible process that would be of particular benefit for projects where a registry is not available, or where time or cost would limit the availability.
BackgroundThe impacts of the built environment on health is a widely studied international area of research. One area of research is how urban morphology (e.g. active living environments, also known ...as neighbourhood walkability) may promote healthy behaviour within a population. However urban morphology and data relating to the built environment varies across different countries.
ObjectivesOne of the challenges in international studies is producing consistent, comparable measures of the built environment, in this case active living environments. As part of a study which compares the impact of neighbourhood environments on health outcomes for patients with type 2 diabetes (T2D), neighbourhood-level measures for walkable environments were derived for Canada and Wales using Geographic Information Systems (GIS).
MethodsUsing method based upon the Canadian Active Living Environments Database (Can-ALE) we created walkability indicators for Wales, UK. We created GIS models using OpenStreetMap and Office for National Statistics (ONS) Open Data to produce walkability metrics for each Lower Layer Super Output Area (LSOA) in Wales for linkage into the SAIL databank. We compared the GIS generated walkability metrics for Wales with those produced for Canada to evaluate whether the GIS methods are internationally transferable in the context of generating walkability indictors and associations with T2D.
FindingsThis work highlights the challenges in creating internationally comparable environmental exposure metrics. The differences in urban morphology and scale in Canada and Wales are significant, however this work demonstrates how with considered methodological choices these differences can be overcome to generate comparable built environment indicators.
ConclusionsThe generation of comparable walkability indicators for the built environment has allowed subsequent analysis into hospital admissions for people living with T2D in Caranda and Wales. This study has wider implications for international research into the impacts of the built environment on population health and are reproducible on future studies.
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.
BACKGROUNDLiving 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.METHODSA 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.FINDINGSAfter 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.INTERPRETATIONAmbient 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.FUNDINGNational Institute for Health and Care Research Public Health Research programme.
The COVID-19 pandemic has placed a spotlight on existing and enduring health inequalities experienced by different ethnic groups. There has been a longstanding call to generate and improve the use of ...ethnicity data available across different data sources, in order to improve our understanding of health risks, behaviours and outcomes.
We used multiple anonymised individual-level population-scale data sources available within the Secure Anonymised Information Linkage (SAIL) Databank to develop a harmonised ethnicity spine for the population of Wales. We documented ethnicity information in multiple longitudinal records from January 2000 onwards. Data sources included: health and social care, birth and mortality records, national census records, specialist clinical audits and registers, surveys and other routine electronic data. To enable multi-source harmonisation, we explored the ethnicity categorisation as well as temporal changes in recording and classifications by obtaining distribution of records for population, which informed our harmonisation algorithm for standardisation of ethnicity records.
We used over 20-data sources on ~5-million individuals, spanning varying time-periods starting from January 2000 upto a maximum of 22-years. We harmonised available recorded ethnicity values into standardised ethnic classification groups within a national ethnicity-spine. Furthermore, we investigated the impact of different harmonisation methods, including composite, latest date of recording, modal and weighted modal results. With the main focus of the methodological development being in response to the COVID-19 pandemic, when linked to the ~3.1 million individuals alive and resident in Wales from January 2020, we generated harmonised ethnic groups towards ~95% completeness in data coverage for the whole population of Wales. The predominant ethnic group in Wales observed was White, accounting for 89% of the population when using the latest date of recording method.
This research highlights challenges in using longitudinal ethnicity data across different sources. Further work is needed to understand the basis on which individuals / organisations record ethnicity overtime. We recommend improvements recognising differences between ethnicity and other social constructs (e.g. ancestry, nationality, country of origin) are better documented / understood.
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.
ObjectivesResearch on bi-directional associations between self-reported caregiver mental health and child development is mixed. Through linkage of a cohort study and primary care data, we examine ...whether maternal mental health diagnoses, treatment and symptoms are bi-directionally associated with child development, namely emotional and conduct problems, hyperactivity and peer problems.
ApproachWe accessed 14 years of data by linking the Millennium Cohort Study (in Wales) to anonymised individual-level population-scale health and administrative data within the Secure Anonymised Information Linkage (SAIL) Databank. We identified maternal mental health problems using John et al’s (2016) existing algorithm for anxiety and depression diagnoses, symptoms and treatment. We measured child development using parent-reports of the Strengths and Difficulties questionnaire. Outcomes were tested when the child was 3, 5, 7, 11 and 14 years of age. We used Bayesian Structural Equation Modelling, specifically Random-Intercept Cross-Lagged Panel Models, to analyse within and between-person associations.
ResultsWe found that mother’s mental health was strongly associated over time, as were children’s development difficulties. Cross-lagged associations from mother to child were weakly positively associated at age 3 to 5 for child total scores, and at age 11 to 14 for child emotional problems. In contrast, child development associations with maternal mental health were significant from age 7 to 11 for total scores, emotional and peer problems, but weakly associated at age 3 to 5, and 11 to 14 for conduct problems. Hyperactivity had few associations. Few associations at the same-time point were found, but emotional problems at age 11 and 14 were positively associated, as were hyperactivity at age 14, and peer problems at age 11. Between-person effects were consistently strongly associated.
ConclusionWe find mixed evidence for bi-directional associations, but strong between-person associations. Overall development and emotional problem models showed more bi-directional relationships; child development was positively associated with mother’s mental health event at age 7 for all models except hyperactivity, conduct problems were weakly associated at age 5 and 11.
BackgroundDemographic profiling is an important aspect of anonymised healthcare research used to identify populations of interest. Typically, administrative data is used in conjunction with patient ...registers to create cohorts, but it can be a time consuming process.
ObjectivesWe aim to create and apply a method of identifying care homes using existing administrative data. We also aim to test the accuracy of our method by comparing the results to a pseudonymised national care home registry. This will allow us to prove whether proxy methods may be of sufficient accuracy for data linkage research in the future.
Methods (including data)Our method uses quantifiable characteristics from longitudinal data to identify potential care homes. This includes the number and age of occupants, current residence and rate of change of occupancy.
ConclusionsThis method is a reproducible process that would be of particular benefit for projects where a registry is not available, or where time or cost would limit the availability. This method can also be generalised to any communal establishment, where often the identification of vulnerable populations (antibiotic resistance, infectious disease etc.) is particularly beneficial.
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