IntroductionThe 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 analysisTwo 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 disseminationThe 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.

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We analyse global data for COVID-19 deaths and recoveries and show that outbreak severity displays a striking latitude relationship with a northern hemisphere bias. Transmission rates can be explained by seasonal weather conditions, but this does not account for observed variations in fatality rates. Many factors point to Vitamin D as a candidate explanation but historical controversy surrounding Vitamin D studies and the lack of a coherent framework for causal inference has hampered acceptance of this explanation despite a wealth of evidence in its favour.We analyse global COVID-19 data using Causal Inference, constructing two contrasting directed acyclic graph (DAG) models, one causal and one acausal, and set out clearly multiple predictions made by each model. We show that observed data strongly match predictions made by the causal model but largely contradict those of the acausal model. We explore historic evidence further supporting the causal model.We review biochemical mechanisms that may explain the various ways in which vitamin D acts. We detail the mechanisms by which the SARS-Cov-2 virus causes the disease and known pathways that involve Vitamin D and show how these both protect against viral infection, as well as ameliorating disease symptoms in COVID-19 and other respiratory diseases.We examine the factors that govern confidence in causal inference models and conclude that a high level of confidence in a causal beneficial role for Vitamin D is justified.

Background The CVD-COVID-UK consortium was formed to understand the relationship between COVID-19 and cardiovascular diseases through analyses of harmonised electronic health records (EHRs) across the four UK nations. Beyond COVID-19, data harmonisation and common approaches enable analysis within and across independent Trusted Research Environments. Here we describe the reproducible harmonisation method developed using large-scale EHRs in Wales to accommodate the fast and efficient implementation of cross-nation analysis in England and Wales as part of the CVD-COVID-UK programme. We characterise current challenges and share lessons learnt. Methods Serving the scope and scalability of multiple study protocols, we used linked, anonymised individual-level EHR, demographic and administrative data held within the SAIL Databank for the population of Wales. The harmonisation method was implemented as a four-layer reproducible process, starting from raw data in the first layer. Then each of the layers two to four is framed by, but not limited to, the characterised challenges and lessons learnt. We achieved curated data as part of our second layer, followed by extracting phenotyped data in the third layer. We captured any project-specific requirements in the fourth layer. Results Using the implemented four-layer harmonisation method, we retrieved approximately 100 health-related variables for the 3.2 million individuals in Wales, which are harmonised with corresponding variables for > 56 million individuals in England. We processed 13 data sources into the first layer of our harmonisation method: five of these are updated daily or weekly, and the rest at various frequencies providing sufficient data flow updates for frequent capturing of up-to-date demographic, administrative and clinical information. Conclusions We implemented an efficient, transparent, scalable, and reproducible harmonisation method that enables multi-nation collaborative research. With a current focus on COVID-19 and its relationship with cardiovascular outcomes, the harmonised data has supported a wide range of research activities across the UK.

BackgroundBetter understanding of the role that children and school staff play in the transmission of SARS-CoV-2 is essential to guide policy development on controlling infection while minimising disruption to children’s education and well-being.MethodsOur national e-cohort (n=464531) study used anonymised linked data for pupils, staff and associated households linked via educational settings in Wales. We estimated the odds of testing positive for SARS-CoV-2 infection for staff and pupils over the period August– December 2020, dependent on measures of recent exposure to known cases linked to their educational settings.ResultsThe total number of cases in a school was not associated with a subsequent increase in the odds of testing positive (staff OR per case: 0.92, 95% CI 0.85 to 1.00; pupil OR per case: 0.98, 95% CI 0.93 to 1.02). Among pupils, the number of recent cases within the same year group was significantly associated with subsequent increased odds of testing positive (OR per case: 1.12, 95% CI 1.08 to 1.15). These effects were adjusted for a range of demographic covariates, and in particular any known cases within the same household, which had the strongest association with testing positive (staff OR: 39.86, 95% CI 35.01 to 45.38; pupil OR: 9.39, 95% CI 8.94 to 9.88).ConclusionsIn a national school cohort, the odds of staff testing positive for SARS-CoV-2 infection were not significantly increased in the 14-day period after case detection in the school. However, pupils were found to be at increased odds, following cases appearing within their own year group, where most of their contacts occur. Strong mitigation measures over the whole of the study period may have reduced wider spread within the school environment.

Determining the severity of a novel pathogen in the early stages is difficult in the absence of reliable data. The pattern of outbreaks for COVID-19 across the globe have differed markedly above and below 30°N latitudes, suggesting very different levels of severity, but countries worldwide have implemented the same lockdown strategies. Existing methods for estimating severity appear not to have been useful in informing strategic decisions, possibly due to mismatches between the data required and those available, overly sophisticated methods with undesirable biases, or perhaps confusion and uncertainly generated by the wide range of estimates these methods produced early on.The Epidemic Severity Index (ESI) is a simple, robust method for estimating the local severity of novel epidemic outbreaks using early and widely-available data and that does not depend on any estimated values. ESI allows rapid, meaningful comparisons across territories that can be tracked as the outbreaks unfold. The ESI quantifies severity relative to a parameterised baseline rather than attempting to estimate values for infection fatality rates, case fatality rates or transmission rates. The relative nature of the ESI sidesteps any problems of confidence associated with absolute rate estimation methods and offers immediate practical strategic value.

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.

BackgroundMedication prescribing and dispensing often regarded as one of the most effective ways to manage and improve population health. Prescribed and dispensed medications can be monitored through data linkage for each patient. We hypothesised that changes in patient care resulting from COVID-19, changed the way patients access their prescribed medication.Objective To develop an efficient approach for evaluation of the impact of COVID-19 on drug dispensing patterns.MethodsRetrospective observational study using national patient-level dispensing records in Wales-UK. Total dispensed drug items between 01-Jan-2016 and 31-Dec-2019 (counterfactual pre-COVID-19) were compared to 2020 (COVID-19 year). We compared trends of dispensed items in three main British National Formulary (BNF) sections(Cardiovascular system, Central Nervous System, Immunological & Vaccine) using European Age-Standardized rates. We developed an online tool to enable monitoring of changes in dispensing as the pandemic evolves.ResultAmongst all BNF chapters, 52,357,639 items were dispensed in 2020 compared to 49,747,141 items in 2019 demonstrating a relative increase of 5.25% in 2020(95%CI[5.21,5.29]). Comparison of monthly patterns of 2020 and 2019 dispensed items showed a notable difference between the total number of dispensed drug items each month, with an average difference (D) of +290,055 and average Relative Change (RC) of +5.52%. The greatest RC was observed in a substantial March-2020 increase (D=+1,501,242 and RC=+28%), followed by second peak in June (D=+565,004, RC=+10.97%). May was characterised by lower dispensing (D=-399,244, RC=-5.9%). Cardiovascular categories were characterised, across all age groups, by dramatic March-2020 increases, at the epidemic peak, followed by months of lower than expected dispensing, and gradual recovery by September. The Central Nervous System category was similar, but with only a short decline in May, and quicker recovery. A stand-out grouping was Immunological and Vaccine, which dropped to very low levels across all age groups, and all months (including the March dispensing peak).ConclusionsAberration in clinical service delivery 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