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The BNT162b2 mRNA (Pfizer–BioNTech) and ChAdOx1 nCoV-19 (Oxford–AstraZeneca) COVID-19 vaccines have shown high efficacy against disease in phase 3 clinical trials and are now being used in national vaccination programmes in the UK and several other countries. Studying the real-world effects of these vaccines is an urgent requirement. The aim of our study was to investigate the association between the mass roll-out of the first doses of these COVID-19 vaccines and hospital admissions for COVID-19. We did a prospective cohort study using the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19—EAVE II—database comprising linked vaccination, primary care, real-time reverse transcription-PCR testing, and hospital admission patient records for 5·4 million people in Scotland (about 99% of the population) registered at 940 general practices. Individuals who had previously tested positive were excluded from the analysis. A time-dependent Cox model and Poisson regression models with inverse propensity weights were fitted to estimate effectiveness against COVID-19 hospital admission (defined as 1–adjusted rate ratio) following the first dose of vaccine. Between Dec 8, 2020, and Feb 22, 2021, a total of 1 331 993 people were vaccinated over the study period. The mean age of those vaccinated was 65·0 years (SD 16·2). The first dose of the BNT162b2 mRNA vaccine was associated with a vaccine effect of 91% (95% CI 85–94) for reduced COVID-19 hospital admission at 28–34 days post-vaccination. Vaccine effect at the same time interval for the ChAdOx1 vaccine was 88% (95% CI 75–94). Results of combined vaccine effects against hospital admission due to COVID-19 were similar when restricting the analysis to those aged 80 years and older (83%, 95% CI 72–89 at 28–34 days post-vaccination). Mass roll-out of the first doses of the BNT162b2 mRNA and ChAdOx1 vaccines was associated with substantial reductions in the risk of hospital admission due to COVID-19 in Scotland. There remains the possibility that some of the observed effects might have been due to residual confounding. UK Research and Innovation (Medical Research Council), Research and Innovation Industrial Strategy Challenge Fund, Health Data Research UK.

Current UK vaccination policy is to offer future COVID-19 booster doses to individuals at high risk of serious illness from COVID-19, but it is still uncertain which groups of the population could benefit most. In response to an urgent request from the UK Joint Committee on Vaccination and Immunisation, we aimed to identify risk factors for severe COVID-19 outcomes (ie, COVID-19-related hospitalisation or death) in individuals who had completed their primary COVID-19 vaccination schedule and had received the first booster vaccine. We constructed prospective cohorts across all four UK nations through linkages of primary care, RT-PCR testing, vaccination, hospitalisation, and mortality data on 30 million people. We included individuals who received primary vaccine doses of BNT162b2 (tozinameran; Pfizer–BioNTech) or ChAdOx1 nCoV-19 (Oxford–AstraZeneca) vaccines in our initial analyses. We then restricted analyses to those given a BNT162b2 or mRNA-1273 (elasomeran; Moderna) booster and had a severe COVID-19 outcome between Dec 20, 2021, and Feb 28, 2022 (when the omicron (B.1.1.529) variant was dominant). We fitted time-dependent Poisson regression models and calculated adjusted rate ratios (aRRs) and 95% CIs for the associations between risk factors and COVID-19-related hospitalisation or death. We adjusted for a range of potential covariates, including age, sex, comorbidities, and previous SARS-CoV-2 infection. Stratified analyses were conducted by vaccine type. We then did pooled analyses across UK nations using fixed-effect meta-analyses. Between Dec 8, 2020, and Feb 28, 2022, 17 337 580 individuals completed their primary vaccine schedule and 14 698 030 individuals received a booster dose. Between Dec 20, 2021, and Feb 28, 2022, 59 510 (0·3%) of the primary vaccine group and 26 100 (0·2%) of those who received their booster had severe COVID-19 outcomes. The risk of severe COVID-19 outcomes reduced after receiving the booster (rate change: 8·8 events per 1000 person-years to 7·6 events per 1000 person-years). Older adults (≥80 years vs 18–49 years; aRR 3·60 [95% CI 3·45–3·75]), those with comorbidities (≥5 comorbidities vs none; 9·51 [9·07–9·97]), being male (male vs female; 1·23 [1·20–1·26]), and those with certain underlying health conditions—in particular, individuals receiving immunosuppressants (yes vs no; 5·80 [5·53–6·09])—and those with chronic kidney disease (stage 5 vs no; 3·71 [2·90–4·74]) remained at high risk despite the initial booster. Individuals with a history of COVID-19 infection were at reduced risk (infected ≥9 months before booster dose vs no previous infection; aRR 0·41 [95% CI 0·29–0·58]). Older people, those with multimorbidity, and those with specific underlying health conditions remain at increased risk of COVID-19 hospitalisation and death after the initial vaccine booster and should, therefore, be prioritised for additional boosters, including novel optimised versions, and the increasing array of COVID-19 therapeutics. National Core Studies–Immunity, UK Research and Innovation (Medical Research Council), Health Data Research UK, the Scottish Government, and the University of Edinburgh.

SARS-CoV-2 infection in children and young people (CYP) can lead to life-threatening COVID-19, transmission within households and schools, and the development of long COVID. Using linked health and administrative data, we investigated vaccine uptake among 3,433,483 CYP aged 5–17 years across all UK nations between 4th August 2021 and 31st May 2022. We constructed national cohorts and undertook multi-state modelling and meta-analysis to identify associations between demographic variables and vaccine uptake. We found that uptake of the first COVID-19 vaccine among CYP was low across all four nations compared to other age groups and diminished with subsequent doses. Age and vaccination status of adults living in the same household were identified as important risk factors associated with vaccine uptake in CYP. For example, 5–11 year-olds were less likely to receive their first vaccine compared to 16–17 year-olds (adjusted Hazard Ratio [aHR]: 0.10 (95%CI: 0.06–0.19)), and CYP in unvaccinated households were less likely to receive their first vaccine compared to CYP in partially vaccinated households (aHR: 0.19, 95%CI 0.13–0.29). COVID-19 vaccination has been recommended for children and young people (aged 5–17) in the UK since 2021/2022. In this study, the authors use linked health and administrative data to estimate vaccine uptake in this age group and show that age and adult household vaccination status are associated with uptake.

At the start of the COVID-19 pandemic there was an urgent need to identify individuals at highest risk of severe outcomes, such as hospitalisation and death following infection. The QCOVID risk prediction algorithms emerged as key tools in facilitating this which were further developed during the second wave of the COVID-19 pandemic to identify groups of people at highest risk of severe COVID-19 related outcomes following one or two doses of vaccine. To externally validate the QCOVID3 algorithm based on primary and secondary care records for Wales, UK. We conducted an observational, prospective cohort based on electronic health care records for 1.66m vaccinated adults living in Wales on 8th December 2020, with follow-up until 15th June 2021. Follow-up started from day 14 post vaccination to allow the full effect of the vaccine. The scores produced by the QCOVID3 risk algorithm showed high levels of discrimination for both COVID-19 related deaths and hospital admissions and good calibration (Harrell C statistic: ≥ 0.828). This validation of the updated QCOVID3 risk algorithms in the adult vaccinated Welsh population has shown that the algorithms are valid for use in the Welsh population, and applicable on a population independent of the original study, which has not been previously reported. This study provides further evidence that the QCOVID algorithms can help inform public health risk management on the ongoing surveillance and intervention to manage COVID-19 related risks.

ObjectiveThis study aims to create a national ethnicity spine based on all available ethnicity records in linkable anonymised electronic health record and administrative data sources.DesignA longitudinal study using anonymised individual-level population-scale ethnicity data from 26 data sources available within the Secure Anonymised Information Linkage Databank.SettingThe national ethnicity spine is created based on longitudinal national data for the population of Wales-UK over 22 years (between 2000 and 2021).Procedure and participantsA total of 46 million ethnicity records for 4 297 694 individuals have been extracted, harmonised, deduplicated and made available within a longitudinal research ready data asset.Outcome measures(1) Comparing the distribution of ethnicity records over time for four different selection approaches (latest, mode, weighted mode and composite) across age bands, sex, deprivation quintiles, health board and residential location and (2) distribution and completeness of records against the ONS census 2011.ResultsThe distribution of the dominant group (white) is minimally affected based on the four different selection approaches. Across all other ethnic group categorisations, the mixed group was most susceptible to variation in distribution depending on the selection approach used and varied from a 0.6% prevalence across the latest and mode approach to a 1.1% prevalence for the weighted mode, compared with the 3.1% prevalence for the composite approach. Substantial alignment was observed with ONS 2011 census with the Latest group method (kappa=0.68, 95% CI (0.67 to 0.71)) across all subgroups. The record completeness rate was over 95% in 2021.ConclusionIn conclusion, our development of the population-scale ethnicity spine provides robust ethnicity measures for healthcare research in Wales and a template which can easily be deployed in other trusted research environments in the UK and beyond.

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.

This article presents data from the first large-scale study of fathers involved in repeat (or recurrent) care proceedings in England. The project complements important research on mothers and recurrence. It consisted of three elements: an analysis of population-level administrative data from the Child and Family Court Advisory and Support Service (CAFCASS), a survey of fathers in pre-proceedings and care proceedings, and a qualitative longitudinal (QL) study of recurrent fathers. Here we report findings from the survey and the QL study, offering an expanded definition and description of fathers and recurrence. Elsewhere, we reported that a significant number of fathers appear in recurrent care proceedings and that the majority return with the same partner. Alongside this, there is also a notable pattern of “missing” fathers demonstrated by the proportion of lone mothers reappearing before the court. Our survey indicates a certain profile of recurrent fathers, but also that recurrent fathers are not straightforwardly a homogenous group. We report on the significance of recurrent fathers’ early lives, on the phenomenon of enduring couple relationships and on the prevalence of issues affecting parenting, such as poor mental health, substance use and domestic abuse. Insights from the QL study in particular reveal legacies of harm, loss, and a lack of emotional and relational resources in childhood, which have debilitating and far-reaching consequences. We argue the importance of understanding the vulnerabilities of recurrent fathers and of challenging certain assumptions in child welfare and family justice practices. There is much to be learnt from existing services for recurrent mothers, but also a need for bespoke or adapted services that may be more responsive to particular circumstances of recurrent fathers and couples.

We noted that there remains some confusion in the health-science literature on reporting sample odds ratios as estimated rate ratios in case-control studies. We recap historical literature that definitively answered the question of when sample odds ratios (ORs) from a case-control study are consistent estimators for population rate ratios. We use numerical examples to illustrate the magnitude of the disparity between sample ORs in a case-control study and population rate ratios when sufficient conditions for them to be equal are not satisfied. We stress that in a case-control study, sampling controls from those still at risk at the time of outcome event of the index case is not sufficient for a sample OR to be a consistent estimator for an intelligible rate ratio. In such studies, constancy of the exposure prevalence together with constancy of the hazard ratio (HR) (i.e., the instantaneous rate ratio) over time is sufficient for this result if sampling time is not controlled; if time is controlled, constancy of the HR will suffice. We present numerical examples to illustrate how failure to satisfy these conditions adds a small systematic error to sample ORs as estimates of population rate ratios. We recommend that researchers understand and critically evaluate all conditions used to interpret their estimates as consistent for a population parameter in case-control studies.

Vaccines against COVID-19 and influenza can reduce the adverse outcomes caused by infections during pregnancy, but vaccine uptake among pregnant women has been suboptimal. We examined the COVID-19 and influenza vaccine uptake and disparities in pregnant women during the COVID-19 pandemic to inform vaccination interventions. We used data from the Oxford-Royal College of General Practitioners Research and Surveillance Centre database in England and the Secure Anonymised Information Linkage Databank in Wales. The uptake of at least one dose of vaccine was 40.2% for COVID-19 and 41.8% for influenza among eligible pregnant women. We observed disparities in COVID-19 and influenza vaccine uptake, with socioeconomically deprived and ethnic minority groups showing lower vaccination rates. The suboptimal uptake of COVID-19 and influenza vaccines, especially in those from socioeconomically deprived backgrounds and Black, mixed or other ethnic groups, underscores the necessity for interventions to reduce vaccine hesitancy and enhance acceptance in pregnant women.

IntroductionThe 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 analysisWe 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 disseminationWe 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.