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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.

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.

IntroductionCystic fibrosis (CF) is the most common life-limiting inherited disease in white populations, with most patients dying prematurely from respiratory failure. As it is rare, it is important to reduce misclassification. We therefore aimed to assess how well CF children could be identified across routine data in Wales.
Objectives and ApproachData from the Secure Anonymised Information Linkage (SAIL) databank, identified children with CF from 1998 to 2016, within hospital, General Practice (GP), and the Welsh Congenital Abnormality Register (CARIS), which uses new born screening to identify congenital abnormalities, including CF. The International Classification of Diseases (ICD10) E84 was used to identify CF children in both hospital and CARIS data, with READ codes used in the GP data (approximately 80% coverage of Wales). The data was linked using anonymised linking fields and matching rates analysed, as unmatched records in linked data can reduce the utility of the data for epidemiological studies.
Results352 cases were identified in total, with 158 matched across all three datasets over an 18 year period (9-19 cases per year). The Welsh rate from the disease registry is 12–14. Since CF is a severely debilitating condition, a greater match was expected. This prompted further investigation of cases which appeared in only one dataset, as these seemed least likely to be true cases. In the 'CARIS only' data, 79% of the admissions, were found coded for respiratory, digestive and health complications not for an E84 CF condition. In the 43 cases in 'GP only' data, and the 19 in 'hospital only' data the events indicated the possible late presentation of CF by older children or children with very mild CF phenotypes.
Conclusion/ImplicationsCases of rare diseases like CF can be identified in routine data. Linking across multiple datasets, particularly with specialist datasets like CARIS, help identify potentially misclassified cases. This increases confidence in the data. Future work will include the CF registry, permitting checks against a gold standard data resource.

IntroductionSoft Tissue Sarcoma (STS) diagnosis is difficult due to its nature and the variability of its occurrence on the body. To improve patient outcomes a better understanding was needed of the care pathways experienced by the patient from initial presentation to final treatment.
Objectives and ApproachSeveral items of information are necessary, within the data, to identify a care pathway. A correct STS diagnosis, a presentation date or first investigation date, a diagnosis date and any subsequent treatment dates. Identifying cases in hospital data, using International Classification of Diseases (ICD10) codes - C40, C41, C47 and C49 - based on cancer site - can miss cases and cause difficulties when trying to distinguish the difference between the investigation and treatment stages. Having access to WCISU's national cancer registry, proved advantageous and enabled the routine data to be validated.
ResultsAttempts to identify differences between investigative and treatment procedures using the procedure codes available in hospital data was unhelpful due to variations in coding.
However, WCISU's national cancer registry records all cases of cancer diagnosed in Wales using both ICD10 and International Classification of Diseases for Oncology codes to record cancer morphology. In addition, it records the date of diagnosis and treatment start dates. Using the cancer registry it was possible to cross-check the cases extracted from the hospital data and identify the diagnosis and treatment dates. By matching the treatment dates back to the hospital data it then became possible to analyse the procedure codes to see how many treatments were being delivered, the type of treatment and the periods covered.
Conclusion/ImplicationsOnce accurate diagnosis and treatments dates were identified, it was possible to drill further into the hospital data to see the finer detail of the procedures the patient received. Utilising independent data sources made it possible to develop an enriched view of patient care pathways from diagnosis through to treatment.

Introduction
The challenges in identifying a cohort of people with a rare condition can be addressed by routinely collected, population-scale electronic health record (eHR) data, which provide large volumes of data at a national level. This paper describes the challenges of accurately identifying a cohort of children with Cystic Fibrosis (CF) using eHR and their validation against the UK CF Registry.
Objectives
To establish a proof of principle and provide insight into the merits of linked data in CF research and the benefits of access to multiple data sources. In particular, the UK CF Registry data, and to demonstrate the opportunity it represents as a resource for future CF research.

Method
Three eHR data sources were used to identify children with CF born in Wales between 1st January 1998 and 31st August 2015 within the Secure Anonymised Information Linkage (SAIL) Databank. The UK CF Registry was later acquired by SAIL and linked to the eHR cohort to validate the cases and explore the reasons for misclassifications.
Results
We identified 352 children with CF in the three eHR data sources. This was greater than expected based on historical incidence rates in Wales. Subsequent validation using the UK CF Registry found that 257 (73%) of these were true cases. Over 98% of individuals identified as CF cases in all three eHR data sources were confirmed as true cases; but this was only the case for 19.8% of those identified in a single data source.
Conclusion
Identifying health conditions in eHR data can be challenging, so data quality assurance and validation is important or the merit of the research is undermined. This retrospective review identifies some of the challenges in identifying CF cases and demonstrates the benefits of linking cases across multiple data sources to improve quality.

IntroductionCritical Care is a specialty in medicine providing a service for severely ill and high-risk patients who, due to the nature of their condition, may require long periods recovering after discharge. Consequently, focus on the routine data collection carried out in Intensive Care Units (ICUs) leads to reporting that is confined to the critical care episode and is typically insensitive to variation in individual patient pathways through critical care to recovery.
A resource which facilitates efficient research into interactions with healthcare services surrounding critical admissions, capturing the complete patient's healthcare trajectory from primary care to non-acute hospital care prior to ICU, would provide an important longer-term perspective for critical care research.
ObjectiveTo describe and apply a reproducible methodology that demonstrates how both routine administrative and clinically rich critical care data sources can be integrated with primary and secondary healthcare data to create a single dataset that captures a broader view of patient care.
MethodTo demonstrate the INTEGRATE methodology, it was applied to routine administrative and clinical healthcare data sources in the Secure Anonymised Data Linking (SAIL) Databank to create a dataset of patients' complete healthcare trajectory prior to critical care admission. SAIL is a national, data safe haven of anonymised linkable datasets about the population of Wales.
ResultsWhen applying the INTEGRATE methodology in SAIL, between 2010 and 2019 we observed 91,582 critical admissions for 76,019 patients. Of these, 90,632 (99%) had an associated non-acute hospital admission, 48,979 (53%) hadan emergency admission, and 64,832 (71%) a primary care interaction in the week prior to the critical care admission.
ConclusionThis methodology, at population scale, integrates two critical care data sources into a single dataset together with data sources on healthcare prior to critical admission, thus providing a key research asset to study critical care pathways.

ObjectivesVarious analgesics are frequently prescribed to cancer patients for whom pain contributes to poor physical and emotional health and well-being. We examined changes in trends of analgesic prescribing in over 35,000 cancer patients diagnosed in the Welsh population before and during the COVID-19 pandemic in order to gain insight into the COVID-19 pandemic effects on cancer patients' ability to receive analgesia and their potential ability to control their pain via medications.
ApproachWithin the Secure Anonymised Information Linkage (SAIL) Databank trusted research environment (TRE), patients diagnosed with incident primary breast, lung, colorectal or prostate cancers during 2017–2021 were obtained from Cancer Network Information System Cymru (CaNISC) dataset and patients' prescription records were identified from Welsh Longitudinal General Practitioner (WLGP) dataset before being linked to their oncology e-record. We calculated opioid and non-opioid analgesic items prescribed per patient per year (PPPY) since cancer by clinical and demographic factors including cancer type, stage at diagnosis, diagnosis year, age at diagnosis, sex, comorbidities and patients' socioeconomic status. These factors were included to model the effects of the COVID-19 pandemic on trends in analgesic prescribing for each cancer group.
ResultsWe detected significant differences in the number of analgesic items prescribed PPPY in patients diagnosed before the COVID-19 pandemic (2017–2019) and those during the pandemic (2020–2021), with 1.3 more items PPPY prescribed for the latter group (p<0.001). Differences were accounted for largely by prescriptions for lung cancer patients, having 2.74 more items PPPY prescribed (p<0.001), the highest among the four cancer types evaluated. Patients diagnosed with a late-stage cancer had significantly more items prescribed than patients diagnosed at an early stage (p<0.001), with stage IV patients having 15.7 opioid items PPPY prescribed. For patients diagnosed at stage I, this rate PPPY was 6.7. Significant differences were also identified between patients from different socioeconomic backgrounds (p<0.001), with patients from the most deprived areas prescribed 11.3 items PPPY, 5.8 more than those from the least deprived areas.
ConclusionsThe significant impact of COVID-19 pandemic on pain medication prescribing for cancer patients could be partly related to the impact of COVID-19 lockdowns on presentation, waiting lists and diagnosis timings, and access to healthcare for prescriptions after diagnosis. Explanatory factors revealed by this study can help inform policymakers and provide guidance in improving pain relief for cancer services.

IntroductionCOVID-19 risk prediction algorithms can be used to identify at-risk individuals from short-term serious adverse COVID-19 outcomes such as hospitalisation and death. It is important to validate these algorithms in different and diverse populations to help guide risk management decisions and target vaccination and treatment programs to the most vulnerable individuals in society.
ObjectivesTo validate externally the QCOVID risk prediction algorithm that predicts mortality outcomes from COVID-19 in the adult population of Wales, UK.
MethodsWe conducted a retrospective cohort study using routinely collected individual-level data held in the Secure Anonymised Information Linkage (SAIL) Databank. The cohort included individuals aged between 19 and 100 years, living in Wales on 24th January 2020, registered with a SAIL-providing general practice, and followed-up to death or study end (28th July 2020). Demographic, primary and secondary healthcare, and dispensing data were used to derive all the predictor variables used to develop the published QCOVID algorithm. Mortality data were used to define time to confirmed or suspected COVID-19 death. Performance metrics, including R2 values (explained variation), Brier scores, and measures of discrimination and calibration were calculated for two periods (24th January–30th April 2020 and 1st May–28th July 2020) to assess algorithm performance.
Results1,956,760 individuals were included. 1,192 (0.06%) and 610 (0.03%) COVID-19 deaths occurred in the first and second time periods, respectively. The algorithms fitted the Welsh data and population well, explaining 68.8% (95% CI: 66.9-70.4) of the variation in time to death, Harrell's C statistic: 0.929 (95% CI: 0.921-0.937) and D statistic: 3.036 (95% CI: 2.913-3.159) for males in the first period. Similar results were found for females and in the second time period for both sexes.
ConclusionsThe QCOVID algorithm developed in England can be used for public health risk management for the adult Welsh population.

BackgroundPopulation-level information on dispensed medication provides insight on the distribution of treated morbidities, particularly if linked to other population-scale data at an individual-level.
ObjectiveTo evaluate the impact of COVID-19 on dispensing patterns of medications.
MethodsRetrospective observational study using population-scale, individual-level dispensing records in Wales, UK. Total dispensed drug items for the population between 1st January 2016 and 31st December 2019 (3-years, pre-COVID-19) were compared to 2020 with follow up until 27th July 2021 (COVID-19 period). We compared trends across all years and British National Formulary (BNF) chapters and highlighted the trends in three major chapters for 2019-21: 1-Cardiovascular system (CVD); 2-Central Nervous System (CNS); 3-Immunological & Vaccine. We developed an interactive dashboard to enable monitoring of changes as the pandemic evolves.
ResultAmongst all BNF chapters, 73,410,543 items were dispensed in 2020 compared to 74,121,180 items in 2019 demonstrating -0.96% relative decrease in 2020. Comparison of monthly patterns showed average difference (D) of -59,220 and average Relative Change (RC) of -0.74% between the number of dispensed items in 2020 and 2019. Maximum RC was observed in March 2020 (D= +1,224,909 and RC= +20.62%), followed by second peak in June 2020 (D= +257,920, RC= +4.50%). A third peak was observed in September 2020 (D= +264,138, RC= +4.35%). Large increases in March 2020 were observed for CVD and CNS medications across all age groups. The Immunological and Vaccine products dropped to very low levels across all age groups and all months (including the March dispensing peak).
ConclusionsReconfiguration of routine clinical services during COVID-19 led to substantial changes in community pharmacy drug dispensing. This change may contribute to a long-term burden of COVID-19, raising the importance of a comprehensive and timely monitoring of changes for evaluation of the potential impact on clinical care and outcomes.

Funding: This research is part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (HDRUK2020.146). EAVE II is funded by the Medical Research Council (MC_PC_19075) and supported by the Scottish Government. This work is supported by BREATHE - The Health Data Research Hub for Respiratory Health (MC_PC_19004). BREATHE is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. ConCOV is supported by the Medical Research Council (MR/V028367/1); Health Data Research UK (HDR-9006) which receives its funding from the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation (BHF) and the Wellcome Trust; and Administrative Data Research UK which is funded by the Economic and Social Research Council (grant ES/S007393/1). ; Introduction : The novel coronavirus SARS-CoV-2, which emerged in December 2019, has caused millions of deaths and severe illness worldwide. Numerous vaccines are currently under development of which a few have now been authorised for population-level administration by several countries. As of 20 September 2021, over 48 million people have received their first vaccine dose and over 44 million people have received their second vaccine dose across the UK. We aim to assess the uptake rates, effectiveness, and safety of all currently approved COVID-19 vaccines in the UK. Methods and analysis : We will use prospective cohort study designs to assess vaccine uptake, effectiveness and safety against clinical outcomes and deaths. Test-negative case–control study design will be used to assess vaccine effectiveness (VE) against laboratory confirmed SARS-CoV-2 infection. Self-controlled case series and retrospective cohort study designs will be carried out to assess vaccine safety against mild-to-moderate and severe adverse events, respectively. Individual-level pseudonymised data from primary care, secondary care, laboratory test and death records will be linked and analysed in secure research environments in each UK nation. Univariate and multivariate logistic regression models will be carried out to estimate vaccine uptake levels in relation to various population characteristics. VE estimates against laboratory confirmed SARS-CoV-2 infection will be generated using a generalised additive logistic model. Time-dependent Cox models will be used to estimate the VE against clinical outcomes and deaths. The safety of the vaccines will be assessed using logistic regression models with an offset for the length of the risk period. Where possible, data will be meta-analysed across the UK nations. Ethics and dissemination : We obtained approvals from the National Research Ethics Service Committee, Southeast Scotland 02 (12/SS/0201), the Secure Anonymised Information Linkage independent Information Governance Review Panel project number 0911. Concerning English data, University of Oxford is compliant with the General Data Protection Regulation and the National Health Service (NHS) Digital Data Security and Protection Policy. This is an approved study (Integrated Research Application ID 301740, Health Research Authority (HRA) Research Ethics Committee 21/HRA/2786). The Oxford-Royal College of General Practitioners Clinical Informatics Digital Hub meets NHS Digital's Data Security and Protection Toolkit requirements. In Northern Ireland, the project was approved by the Honest Broker Governance Board, project number 0064. Findings will be made available to national policy-makers, presented at conferences and published in peer-reviewed journals. ; Publisher PDF ; Peer reviewed