IntroductionMultimorbidity is widely recognised as the presence of two or more concurrent long-term conditions, yet remains a poorly understood global issue despite increasing in prevalence.We have ...created the Wales Multimorbidity e-Cohort (WMC) to provide an accessible research ready data asset to further the understanding of multimorbidity. Our objectives are to create a platform to support research which would help to understand prevalence, trajectories and determinants in multimorbidity, characterise clusters that lead to highest burden on individuals and healthcare services, and evaluate and provide new multimorbidity phenotypes and algorithms to the National Health Service and research communities to support prevention, healthcare planning and the management of individuals with multimorbidity.Methods and analysisThe WMC has been created and derived from multisourced demographic, administrative and electronic health record data relating to the Welsh population in the Secure Anonymised Information Linkage (SAIL) Databank. The WMC consists of 2.9 million people alive and living in Wales on the 1 January 2000 with follow-up until 31 December 2019, Welsh residency break or death. Published comorbidity indices and phenotype code lists will be used to measure and conceptualise multimorbidity.Study outcomes will include: (1) a description of multimorbidity using published data phenotype algorithms/ontologies, (2) investigation of the associations between baseline demographic factors and multimorbidity, (3) identification of temporal trajectories of clusters of conditions and multimorbidity and (4) investigation of multimorbidity clusters with poor outcomes such as mortality and high healthcare service utilisation.Ethics and disseminationThe SAIL Databank independent Information Governance Review Panel has approved this study (SAIL Project: 0911). Study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals.
The uptake of COVID-19 vaccination in Wales is high at a population level but many inequalities exist. Household composition may be an important factor in COVID-19 vaccination uptake due to the ...practical, social, and psychological implications associated with different living arrangements. In this study, the role of household composition in the uptake of COVID-19 vaccination in Wales was examined with the aim of identifying areas for intervention to address inequalities. Records within the Wales Immunisation System (WIS) COVID-19 vaccination register were linked to the Welsh Demographic Service Dataset (WDSD; a population register for Wales) held within the Secure Anonymised Information Linkage (SAIL) databank. Eight household types were defined based on household size, the presence or absence of children, and the presence of single or multiple generations. Uptake of the second dose of any COVID-19 vaccine was analysed using logistic regression. Gender, age group, health board, rural/urban residential classification, ethnic group, and deprivation quintile were included as covariates for multivariable regression. Compared to two-adult households, all other household types were associated with lower uptake. The most significantly reduced uptake was observed for large, multigenerational, adult group households (aOR 0.45, 95%CI 0.43-0.46). Comparing multivariable regression with and without incorporation of household composition as a variable produced significant differences in odds of vaccination for health board, age group, and ethnic group categories. These results indicate that household composition is an important factor for the uptake of COVID-19 vaccination and consideration of differences in household composition is necessary to mitigate vaccination inequalities.
IntroductionChildhood 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 analysisThis 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 disseminationThis 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.
ObjectivesA defining feature of the COVID-19 pandemic in many countries were the tragic extent to which care home residents were affected and the difficulty in preventing the introduction and ...subsequent spread of infection.
ApproachUtilising linked data in the SAIL Databank we set out to develop a linked data platform as part of the ‘One Wales’ approach to generate evidence to inform policy makers on the key areas of transmission pathways, care home characteristics, excess mortality, and the impacts of vaccination. We used multi-sectoral linked data including routinely collected health data, administrative data and GIS generated metrics on care home characteristics and community infection rates to better understand how multiple factors impacted on care home residents.
ResultsWe created a care home index with enhanced care home characteristics for all care homes in Wales and were able to link 15,773 care home residents in the SAIL Databank to 923 care homes. We were able to generate early evidence demonstrating an increased risk of mortality for care home residents during Wave 1 (adjusted HR 1.72 compared to 2016). We were able to show that hospital discharge in Wales during the initial stages of the pandemic, although significant, had a much smaller impact on subsequent infections than care home size and accounted for 1.8% of infected discharge events. We also showed that community prevalence, inpatient appointments and people living with dementia all contributed to increased risks of catching COVID in a care home.
ConclusionThe response of the ‘One Wales’ team and the SAIL linked data platform facilitated meaningful insight on the impacts of COVID in social care settings in Wales. The evidence generated was used by policy makers from Welsh and UK Governments to inform policy direction as the pandemic progressed.
Rationale: Previous studies indicate active living environments (ALEs) are associated with higher physical activity levels across different geographic contexts, and could lead to reductions in ...hospital burden. Both Wales UK and Canada have advanced data infrastructure that allows record linkage between survey data and administrative health information.
To assess the relationship between ALEs and hospitalization in Wales and Canada.
We performed a population-based comparison using individual-level survey data from the Welsh Health Survey (N = 9968) linked to the Patient Episode Database for Wales, and the Canadian Community Health Survey (N = 40,335) linked to the Discharge Abstract Database. Using equivalent protocols and open-source data for street networks, destinations, and residential density, we derived 5-class measures of the ALE for Wales and Canada (classed 1 through 5, considered least favourable to most favourable for active living, respectively). We evaluated relationships of ALEs to health, behaviours and hospitalization using multivariate regression (reference group was the lowest ALE class 1, considered least favourable for active living).
For Canada, those living in the highest ALE class 5 had lower odds of all-cause hospitalization (OR 0.66, 95% CI 0.54 to 0.81; as compared to the lowest ALE class 1). In contrast, those living in the highest ALE class 5 in Wales had higher odds of all-cause hospitalization (OR 1.37, 95% CI 1.04 to 1.80). The relationship between ALEs and cardiometabolic hospitalization was inconclusive for Canada (OR 0.75, 95% CI 0.50 to 1.12), but we observed higher odds of cardiometabolic hospitalization for respondents living in higher ALE classes for Wales (OR 1.46, 95% CI 1.10 to 1.78; comparing ALE class 4 to ALE class 1).
Canadian respondents living in high ALE neighbourhoods that are understood to be favourable for active living had lower odds of all-cause hospitalization, whereas Welsh respondents living in high ALEs that were deemed favourable for active living exhibited higher odds of all-cause hospitalization. Environments which promote physical activity in one geographic context may not do so in another. There remains a need to identify relevant context-specific factors that encourage active living.
•Active living environments may support physical activity and population health across geographic contexts.•The UK and Canada share similarities in population health trends.•Living in more favourable neighbourhoods was associated with lower hospitalization in Canada. The opposite was true for Wales.•Environmental characteristics which promote population health in one place may not do so in another.
IntroductionResearch involving care homes is often difficult due to a lack of data and ethical issues. Wales (United Kingdom) contains approximately 1.3million residences, of these 717 are officially ...recorded as care homes for older people.
Objectives and ApproachOur objective was to develop a predictive methodology for identifying care homes in administrative data.
We used two data sources within the Secure Anonymised Information Linkage Databank to conduct our study. The Welsh Demographic Service Dataset (WDSD) contains all residences in Wales and demographic details of their occupants. An anonymised dataset of deterministically matched care home addresses was used to determine which of the residences in the WDSD were care homes.
We used details in the WDSD to determine the average age of the occupants, the number of people who moved into the residence in a year, and the number of people who died in a year. We were interested in care homes for older people and restricted all the residences in the WDSD to only those with an average age of 50+ years. We applied logistic regression to determine a probabilistic match for care homes based on the above characteristics. We determined an optimal cut-point for the probability of a residence being a care home based on the sensitivity and specificity.
ResultsRestricting the WDSD to have an average age of occupants of 50+ created a dataset of 3,939 residences, containing 562 care homes. After applying the logistic model to predict the care homes, we found an optimal probability cut-point which resulted in 548 true positives, 105 false positives, 14 false negatives, and 3,272 true negatives.
ImplicationsIdentification of care homes in an anonymised databank using only demographic data allows research into healthcare pathways for this hard to reach and under-researched population.
IntroductionRepresenting patient-registered addresses as pseudonymised Unique Property Reference Numbers (UPRNs) enables linkage of environmental and household information to electronic health ...records (EHRs). However, the accuracy and potential biases in address-matching algorithm results applied to patient addresses is unknown.
Objectives and ApproachTo investigate accuracy, match rate, and biases in assigning UPRNs to general practitioner (GP)-registered patient addresses for a geographically-defined UK population, using a bespoke deterministic address-matching algorithm comprising 213 rules applied in rank order of minimising false-positives, developed for the Discovery Data Service.
We ran this algorithm to match 906,220 adult patient GP-registered addresses (48% female, 47% non-White, 89% 20-64) sampled in mid-2018 from 159 GP practices in four London boroughs to Ordnance Survey’s AddressBase Premium database.
We evaluated the error rates using a gold-standard dataset. We used binary logistic regression to estimate the likelihood (Odds Ratio OR; 95% Confidence Intervals CI) of no UPRN match according to and adjusting for patient age, sex, ethnic background, deprivation, residential mobility and multiple GP registrations.
Results96% of patient addresses were successfully assigned a UPRN. Algorithm sensitivity, specificity, positive and negative predictive-values and F-measure were, respectively: 0.993, 0.019, 0.914, 0.204, and 0.9516.
After mutual adjustment, UPRN assignment was less likely for: men (OR: 0.87; 95%CI: 0.83,0.91); adolescents and the elderly (15-19 years: 0.57;0.43,0.77; ≥90 years: 0.39;0.18,0.84); those from Chinese ethnic backgrounds (0.87;0.8,0.91), living in the least deprived areas (0.25;0.21,0.31), or with two or more distinct UPRNs across multiple registrations (0.37;0.28,0.49); and more likely for: those from Bangladeshi ethnic backgrounds (1.79;1.61,2.00), registered before 2018 (5.10;4.42,5.87), or with multiple GP registrations (2.36;1.82,3.05).
Conclusion / ImplicationsThe Discovery open-source algorithm achieves a high accurate match rate and quantifies the demographic groups that may be under-represented among those successfully matched. This is the first time that bias in matching rates for an address-matching algorithm has been evaluated using patient-registered addresses.
Planning information pertaining to the potential visual impacts of proposed construction developments is particularly important in the case of wind farm planning, given the high levels of concern ...amongst members of the public regarding the perceived negative visual impacts of wind turbines on the landscape. Previous research has highlighted the shortcomings associated with traditional visualization techniques used to assess these impacts, and also the means by which such information is then disseminated to the wider public during the consultation stages of the wind farm planning process. This research is concerned with examining the potential of Web‐based mapping and digital landscape visualization techniques for addressing some of these shortcomings. This article reports the findings of a Web‐based survey study designed to evaluate the potential of online GIS‐based approaches for improving the effectiveness and dissemination of wind farm visualizations and enhancing public participation in the wind farm planning process. Results from the survey study add to the research literature by demonstrating how innovative Web‐based approaches have real potential for augmenting existing methods of information provision and public participation in the planning process. The findings of this study are also potentially transferrable to other landscape planning scenarios.
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.
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