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IntroductionThe Inverse Care Law (ICL) programme in Wales was setup to tackle health inequalities. Eligible populations from deprived communities, at higher risk of cardiovascular disease (CVD) were invited to a health-check and offered appropriate lifestyle and clinical interventions.
Objectives and ApproachEvaluation of this programme is vital to ensure that targeted interventions have been received by those most in need, including referrals to lifestyle services and support. The use of longitudinal population-scale routine-data required the development of an approach which was both efficient and cost effective. To achieve this, the Welsh Longitudinal General Practice (WLGP) data held in SAIL Databank was utilised. A programme-specific methodology was agreed by the programme-board and developed so that data collected from GP records prior, during and post health-check accurately identified the eligible population and allowed the effective assessment of lifestyle and clinical risk factors for CVD; poor diet, physical inactivity, smoking and high alcohol intake, so appropriate interventions could be offered.
ResultsWe evaluated the programme from 2015 to 2019 in 70 GP's across the participating Health-Boards, and identified 175,671 individuals eligible by the programme criteria. Substantial preliminary work has been carried out to ensure the specification of outcome measures are both clinically and epidemiologically accurate and relevant. The final report scheduled for release in August-2020, which will evaluate the impact of the programme.
Conclusion / ImplicationsThis ambitious evaluation of a large-scale programme set in the community involving disparate systems and a range of stakeholders, has been both complex and challenging, requiring substantial effort to design and implement. We hope the outcomes and lessons learned from our experience will improve the design, implementation and evaluation of the programme and lead to improvements in services and the quality of life for people in Wales, and provide an exemplar for health care providers worldwide wishing to conduct similar programmes in the future.

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.

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.

IntroductionMulti-morbidity and polypharmacy are increasing but are under investigated. Data linkage has much to offer in understanding trends in prevalence, inter-relationships between variables and impact on healthcare activity. We created Welsh population e-cohorts in 2000 and 2014 to study these issues, using the Secure Anonymised Information Linkage (SAIL) Databank.
Objectives and ApproachThe aim of this study was to measure changing prevalences of multimorbidity, initially through disease chapter prescribing and then to explore the relationship between the number of morbidities recorded in primary care and use of different hospital based outpatient services. Data linkage was used to create cohorts of Welsh residents registered to SAIL providing General Practices (GPs) for at least 360 days in 2000 and 2014. The 13 Read code drug chapters were used to calculate morbidity scores between 0 and 13. Proportional odds or cumulative logit models were used to relate GP recorded morbidities to outpatient attendance patterns.
ResultsThe GP cohorts included 1.6 million and 2.1 million population with 56.6% and 73.4% having at least one recorded morbidity for 2000 and 2014 respectively. In 2014, 5+ morbidities were most prevalent (61.3%) in 85+ year olds and least common (2.7%) in 5-9 year olds. Some 35% of individuals attended at least one outpatient specialty in 2014, varying from 22.4% for 5-9 year olds and 63.2% for 80-84 year olds.
Preliminary modelling results show the number of GP recorded morbidity chapters was strongly related to increasing outpatient attendances at different specialties, e.g. OR 15.3 (95%CI: 15.1-15.4) of being in a higher outpatient attendance category for the 5+ morbidity group relative to the zero morbidity group. Increasing age and female gender were associated with increased numbers of specialists attended whilst deprivation had a more modest impact.
Conclusion/ImplicationsThere has been a large increase in recorded multimorbidity across all age groups in Wales. In this exploratory cross-sectional design, multimorbidity was strongly related to increasing use of outpatient services. Further work is ongoing to define and utilise more refined multimorbidity metrics and incorporate longitudinal designs in analysis.

IntroductionAging populations with increasing frailty have major implications for health services internationally, and evidence-based treatment becomes increasingly important. The development of an electronic Frailty Index (eFI) using routine primary care data facilitates implementation of evidence-based interventions. However, the eFI does not account for time restrictions regarding when information was recorded.
Objectives and ApproachOur aim is to implement and further validate the eFI using the Secure Anonymised Information Linkage (SAIL) databank, introducing refinements based on time restrictions.
Our implementation of the eFI identifies frailty based on 1574 Read codes, which are mapped amongst 36 categories known as deficits. The eFI is based on the internationally established cumulative deficit model, and each deficit contributes equally to the eFI value.
However, although each deficit is equally weighted, only one of them is currently time dependent. We therefore analyse the time at which each deficit is identified, and propose time dependent cut-points based on our findings.
ResultsWe were able to successfully implement the eFI using data from over 400,000 individuals from the Welsh population using data held in the SAIL databank. Our results agree with the baseline characteristics and distributions of frailty found in the original development of the eFI.
We also found that the percentage of individuals identified as frail increased as the number of years of records included was increased. Furthermore, the increase in percentage year by year was almost linear for a number of the deficits. This led to the identification of time bounds for particular deficits, which could help to refine future implementations of the eFI.
Conclusion/ImplicationsOur work validates and refines the eFI, which is a particularly useful resource as it uses existing primary care data to identify frailty, meaning no additional resources are required. Furthermore, our implementation is readily available, meaning that future research related to frailty is easily reproducible and achievable by others.

ObjectivesMulti-morbidity and polypharmacy are increasing and interrelated phenomena but are poorly understood. The aim of this study is to contribute to the understanding of these issues, measure the changing prevalence's of multimorbidity/ polypharmacy and explore the relationship between multimorbidity as recorded in primary care and the use of outpatient services.
MethodsThe Secure Anonymised Information Linkage (SAIL) Databank facilitated linkage techniques to create population based e-cohorts of de-identified Welsh residents. Individuals were registered to a SAIL providing General Practice (GP) for at least 360 days in 2000 and 2014. Categories of morbidity were created using the 13 Read drug code chapters. In an initial cross sectional exploratory analysis proportional odds and cumulative logit models were used to relate GP recorded morbidities to outpatient attendance patterns in the same year.
FindingsThe GP e-cohorts included 1.6 million (2000) and 2.1 million (2001) people, with 56.6% and 73.4% having ≥1 recorded morbidity for 2000 and 2014, respectively. In 2014, groups with 5+ morbidities were most prevalent (61.3%) in 85+ year olds and least (2.7%) in 5-9 year olds. Some 35% of individuals attended ≥1 outpatient specialty in 2014; 22.4% in 5-9 year olds and 63.2% for 80-84 year olds.
Results from preliminary models showed the number of GP recorded morbidities was strongly related to increasing outpatient attendances at different specialties, OR=15.3 (95%CI:15.1-15.4) of being in a higher outpatient attendance category for the 5+ morbidity group relative to the zero morbidity group.
ConclusionPreliminary analysis has shown large increases in GP recorded multimorbidity across Wales over fifteen years and strong relationships and NHS service utilisation in cross-sectional analyses. Further work will include creating more refined definitions for multimorbidity metrics, linkage to hospital admission data, comparisons across healthcare settings and the development of longitudinal models.

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.

BackgroundPerson reported population sample surveys have been used to record information on self-reported health for a number of years and provide a useful information source for studies of disease burdens. Developments since 2013 have led to integration of health-related quality of life and subjective well-being as topic areas in some UK-based health surveys. The relative impact of different morbidities on subjective well-being in the UK though is unclear and has not been extensively researched.
ObjectivesThis descriptive study of a combined sample of two years of population-based data from the Welsh Health Survey (n=11,323) aims to address research questions relating to chronic conditions, self-reported health and well-being. The main questions are whether population surveys under- or overreport chronic conditions, and whether the relative level of mental wellbeing is different for those with chronic conditions to those without. Following this initial analysis it seeks to identify potential risk factors to mental wellbeing for those not affected by selected chronic conditions.
MethodsSurvey data were linked with clinical data contained within the Secure Anonymised Information Linkage (SAIL) Databank using SQL (Structured Query Language). The association between selected chronic conditions and relative subjective well-being was then assessed using SF-36 Mental Component Scores as a measure of mental wellbeing. Analysis was based on contingency tables, graphs and logistic regression in SAS 9.4.
FindingsResults show that some self-reported chronic conditions can more easily be verified than others from clinical data. Aside from the selected chronic conditions, potential risk factors to mental well-being include type 2 diabetes, a history of circulatory diseases, psychoactive substance abuse and hypertension.
ConclusionsLinkage of survey data can provide useful insights into relative levels of self-reported illnesses and subjective well-being but can also be used effectively to explore the risks that other morbidities present to mental wellbeing.

BackgroundAging populations with increasing frailty have major implications for health services, and evidence-based treatment becomes increasingly important. The development of the electronic Frailty Index (eFI) using routine primary care data facilitatesthe implementation of evidence-based interventions and care.
MethodOur implementation of the eFI in the Secure Anonymised Information Linkage (SAIL) databank identifies frailty based on 1574 Read codes, which are mapped amongst 36 categories known as deficits. The eFI is based on a cumulative deficitmodel, and each deficit contributes equally to the eFI value.
FindingsAlthough each deficit is equally weighted, only one is currently time dependent. We therefore analysed the cumulative prevalence of each deficit on a year-by-year basis. This led to the identification of time bounds for particular deficits, which willhelp to refine future implementations of the eFI. We also further validated the eFI using data from over 400,000 individuals held in SAIL.
ConclusionThe eFI is particularly useful as it uses existing data to identify frailty, meaning no additional resources are required. Furthermore, our implementation is readily available, meaning that future research related to frailty is easily achievable by others.

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.

IntroductionHealthcare systems face rising demand and unsustainable cost pressures. In response, health policymakers are adopting Value-Based Health Care (VBHC), targeting available resources to achieve the best possible patient outcomes at the lowest possible cost and actively disinvesting in care of low-value. This requires the evaluation of longitudinal clinical and patient reported outcome measures (PROMs) at an individual-level and population-scale, which can create significant data challenges. Achieving this through routinely collected electronic health record (EHR) data-linkage could facilitate the implementation of VBHC without an unacceptable data burden on patients or health systems and release time for higher-value activities.
ObjectivesOur study tested the ability to report an international, patient-centred outcome dataset (ICHOM-IBD) using only anonymised individual-level population-scale linked electronic health record (EHR) data sources, including clinical and patient-reported outcomes, in a cohort of patients with moderate-to-severe ulcerative colitis (UC), receiving biopharmaceutical therapies ("biologics") in a single, publicly funded, healthcare system.
ResultsWe identified a cohort of 17,632 patients with UC in Wales and a cohort from two Health Boards of 447 patients with UC receiving biologics. 112 of these patients had completed 866 condition-specific PROMs during their biologics treatment. 44 out of 59 (74.6%) items in the ICHOM-IBD could be derived from routinely collected data of which a primary care source was essential for eight items and desirable for 21.
ConclusionsWe demonstrated that it is possible to report most but not all the ICHOM-IBD outcomes using routinely collected data from multiple sources without additional system burden, potentially supporting Value-Based Health Care implementation with population data science. As digital collection of PROMs and use of condition-specific registries grow, greater utility of this approach can be anticipated. We have identified that the availability of longitudinal primary and secondary care data linked with PROMs is essential for this to be possible.

IntroductionWhile some comparative work has provided evidence for a universally positive impact of built environment features that promote physical activity, less is known about chronic diseases and hospitalization among different social contexts and health care systems. Parallel international linkage efforts present an opportunity to study health impacts of the built environment.
Objectives and ApproachThis study compares the impact of neighbourhood environments on health outcomes for patients with type 2 diabetes (T2D) in two countries. Neighborhood-level measures for walkable environments were derived for Canada and Wales using Geographic Information Systems. Hospitalization admissions from routine data sources and linked survey data responses (from the Welsh Health Survey and Canadian Community Health Survey) allow for the generation of population-based descriptive statistics on socio-demographic information, self-reported health, diagnostic patterns, and health care use. We examine the feasibility of investigating contextual differences in walkable environments, T2D, and hospitalization between Wales and Canada.
ResultsData linkage in respective privacy protecting safe havens in the Canada Research Data Centre Network (CRDCN) and the Wales Secure Anonymised Information Linkage (SAIL) Databank show promise for a comparative study, enabling parallel modelling of environmental and socio-demographic factors with hospitalization data. Both the Canadian and Welsh surveys ask respondents about their current diabetes status, allowing us to compare hospitalization rates and neighborhood effects of those who report having diabetes with the those who do not. Moreover, the linking of survey responses and similarity in geographic scale permitted consistent measurement of walkable environments across countries. Key administrative variables have been identified relating to health and behaviors, such as socio-demographic information, smoking status, and body mass index, and hospitalization metrics in both countries are commensurable.
Conclusion/ImplicationsThe generation of comparable linked datasets, built environment indicators and comparative research for T2D patients will have wider implications for international assessment of the impacts of environment on chronic diseases, and the hospital burden associated with these conditions.

IntroductionHealth Resource Groups (HRGs) are bundles of care that absorb similar financial resources. Variations in hospital length of stay (LOS) of patients with the same admission diagnosis and assigned HRG may reflect differences in pre-admission primary care services received by patients.
Objectives and ApproachWe investigated whether within HRGs, variations of LOS of chronic obstructive pulmonary disease (COPD) admissions were associated with differences in COPD management services received in primary care. Individual-level primary and secondary care data from the Secure Anonymised Information Linkage (SAIL) databank of Wales was used for admissions in 2015 with high-volume (n>30) HRGs. Effects of selected COPD primary care quality surrogate measures and comorbidities on LOS were analysed using a linear regression model adjusted for a modified Charlson co-morbidity index. The effect of completed pulmonary rehabilitation (PR) was analysed in a separate dataset for the Cwm Taf Health Board.
ResultsWe included 77,791 COPD patients, with mean age of 73.4 [SD=11.0], and 51.6% of which were males. Patients who were referred for a PR course prior to admission stayed 0.58 less days (95% CI = [0.18, 0.98], p<0.01), while those who completed a PR course stayed 0.76 less days ([0.25, 1.27], p<0.01). Non-significant associations were found where female patients stayed 0.34 days longer than males ([-0.01, 0.68], p=0.05), patients given flu vaccination stayed 0.39 less days ([-0.07, 0.86], p=0.10), and patients with anxiety diagnosis stayed 0.43 more days ([-0.03, 0.88], p=0.06).
Conclusion/ImplicationsLOS for COPD could potentially be reduced with further targeted services provided in primary care. Countries using HRGs with access to linked primary and secondary care data can unlock care insights, enabling live monitoring of the effectiveness of primary care interventions. This approach can be also used for other conditions.

Objectives'Long COVID' (LC) is broadly defined as signs and symptoms that continue or develop after the acute phase of COVID-19, and can affect cardiovascular, respiratory and other organ systems. Using electronic health records, we investigated clinical coding of LC in primary and secondary care for the population of Wales.
MethodsWe conducted a cohort study for the population of Wales, using anonymised individual-level linked data in the Secure Anonymised Information Linkage (SAIL) Databank. We used the Welsh COVID-19 e-cohort (doi:10.1136/bmjopen-2020-043010), which consists of all people (adults and children) alive and resident in Wales from 1st January 2020. To this e-cohort we linked primary and secondary care, COVID-19 testing, and ethnic group data. We then calculated the proportion of people with a LC diagnosis code (in primary and secondary care data) overall and stratified by demographic variables.
ResultsOf 3.5m residents, 7,696 (0.2%) had a LC clinical diagnosis. Compared with the general population, a higher proportion of people with LC were female, middle age, white, and hospitalised within 28 days of a confirmed COVID-19 infection. LC affected all socioeconomic groups, as assessed using the Welsh Index of Multiple Deprivation. When looking at LC diagnosis codes in primary care, 30.9% of practices in SAIL have not used these codes at all. And the number of recorded events was low until the end of January 2021, after which there was an increase in coding. These findings are likely a substantial underestimate of LC prevalence in Wales. Earlier estimates from self-reported surveys, such as the Office for National Statistics, are much higher, ranging anywhere between 3-5%.
ConclusionLow recording rates of LC and variation between practices could be due to a delay in introducing clinical coding and lack of presentation/recording. Understanding prevalence of LC is vital for addressing the scale of the problem. Therefore developing additional data-driven approaches is necessary to obtain an accurate prevalence estimate.

IntroductionAs people with cystic fibrosis (CF) lead longer, healthier lives, educational qualifications and employment prospects are increasingly important. However, little is known about the social consequences of CF, in particular, any impact on educational achievements and the support children with CF receive in schools.
ObjectivesTo assess the educational achievements of children with CF in Wales compared to the general Welsh population, and the additional learning support children with CF receive in schools.
MethodsWe conducted a population-scale data linkage study of all children born in Wales using the Secure Anonymised Information Linkage (SAIL) Databank. We used anonymised individual-level population-scale health and administrative data sources to identify children with CF born between 2000 -- 2015, linked to educational attainment records. We calculated the percentage of children that reached expected levels in statutory assessment at age 10-11, Key Stage2 (KS2), and compared this to educational outcomes in the general population. We also assessed the percentage of children with CF that received extra learning support.
ResultsOut of 150 eligible children, 119 had KS2 results. 77% (95% CI: 69%-84%) of children achieved expected levels in English, 81% (95% CI: 73% -87%) in Mathematics and 82% (95% CI: 75% - 88%) in Science. In the comparable general Welsh population, 83.4% to 91.1% achieved the expected level in English, 84.9% to 91.6% in Maths, and 87.1% to 92.2% in Science across the years of the study. 70% of children with CF received extra learning support.
ConclusionsChildren with CF in Wales may have worse educational achievements than the general population. More research is needed to inform policies and interventions to better support children with CF to reach their full educational potential and employment opportunities.