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Objective: Prior studies identified clinical factors associated with increased risk of pancreatic ductal adenocarcinoma (PDAC). However, little is known regarding their time-varying nature, which could inform earlier diagnosis. This study assessed temporality of body mass index (BMI), blood-based markers, comorbidities and medication use with PDAC risk ., Design: We performed a population-based nested case-control study of 28 137 PDAC cases and 261 219 matched-controls in England. We described the associations of biomarkers with risk of PDAC using fractional polynomials and 5-year time trends using joinpoint regression. Associations with comorbidities and medication use were evaluated using conditional logistic regression., Results: Risk of PDAC increased with raised HbA1c, liver markers, white blood cell and platelets, while following a U-shaped relationship for BMI and haemoglobin. Five-year trends showed biphasic BMI decrease and HbA1c increase prior to PDAC; early-gradual changes 2-3 years prior, followed by late-rapid changes 1-2 years prior. Liver markers and blood counts (white blood cell, platelets) showed monophasic rapid-increase approximately 1 year prior. Recent diagnosis of pancreatic cyst, pancreatitis, type 2 diabetes and initiation of certain glucose-lowering and acid-regulating therapies were associated with highest risk of PDAC., Conclusion: Risk of PDAC increased with raised HbA1c, liver markers, white blood cell and platelets, while followed a U-shaped relationship for BMI and haemoglobin. BMI and HbA1c derange biphasically approximately 3 years prior while liver markers and blood counts (white blood cell, platelets) derange monophasically approximately 1 year prior to PDAC. Profiling these in combination with their temporality could inform earlier PDAC diagnosis., Competing Interests: Competing interests: JH-C reports grants from National Institute for Health Research (NIHR) Biomedical Research Centre, Oxford, grants from John Fell Oxford University Press Research Fund, grants from Cancer Research UK (CR-UK) grant number C5255/A18085, through the Cancer Research UK Oxford Centre, grants from the Oxford Wellcome Institutional Strategic Support Fund (204826/Z/16/Z) and other research councils, during the conduct of the study. JH-C is an unpaid director of QResearch, a not-for-profit organisation which is a partnership between the University of Oxford and EMIS Health who supply the QResearch database used for this work. JH-C is a founder and shareholder of ClinRisk ltd and was its medical director until 31st May 2019. ClinRisk Ltd produces open and closed source software to implement clinical risk algorithms (outside this work) into clinical computer systems. AC reports consulting fees from Mendelian, outside the scope of the current work. RB-R is a cofounder of Alchemab Therapeutics and consultant for Alchemab Therapeutics and GSK. PST reports previous consultation with AstraZeneca and Duke-NUS outside the current work., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

Background: Heterogeneous studies have demonstrated ethnic inequalities in the risk of SARS-CoV-2 infection and adverse COVID-19 outcomes. This study evaluates the association between ethnicity and COVID-19 outcomes in two large population-based cohorts from England and Canada and investigates potential explanatory factors for ethnic patterning of severe outcomes., Methods: We identified adults aged 18 to 99 years in the QResearch primary care (England) and Ontario (Canada) healthcare administrative population-based datasets (start of follow-up: 24th and 25th Jan 2020 in England and Canada, respectively; end of follow-up: 31st Oct and 30th Sept 2020, respectively). We harmonised the definitions and the design of two cohorts to investigate associations between ethnicity and COVID-19-related death, hospitalisation, and intensive care (ICU) admission, adjusted for confounders, and combined the estimates obtained from survival analyses. We calculated the 'percentage of excess risk mediated' by these risk factors in the QResearch cohort., Results: There were 9.83 million adults in the QResearch cohort (11,597 deaths; 21,917 hospitalisations; 2932 ICU admissions) and 10.27 million adults in the Ontario cohort (951 deaths; 5132 hospitalisations; 1191 ICU admissions). Compared to the general population, pooled random-effects estimates showed that South Asian ethnicity was associated with an increased risk of COVID-19 death (hazard ratio: 1.63, 95% CI: 1.09-2.44), hospitalisation (1.53; 1.32-1.76), and ICU admission (1.67; 1.23-2.28). Associations with ethnic groups were consistent across levels of deprivation. In QResearch, sociodemographic, lifestyle, and clinical factors accounted for 42.9% (South Asian) and 39.4% (Black) of the excess risk of COVID-19 death., Conclusion: International population-level analyses demonstrate clear ethnic inequalities in COVID-19 risks. Policymakers should be cognisant of the increased risks in some ethnic populations and design equitable health policy as the pandemic continues., (© 2023. The Author(s).)

Introduction: Dysmenorrhoea affects up to 70%-91% of adolescents who menstruate, with approximately one-third experiencing severe symptoms with impacts on education, work and leisure. Dysmenorrhoea can occur without identifiable pathology, but can indicate underlying conditions, including congenital genital tract anomalies or endometriosis. There is a need for evidence about the management and incidence of dysmenorrhoea in primary care, the impact of treatments in adolescence on long-term outcomes and when to consider the possibility of endometriosis in adolescence., Methods and Analysis: This study aims to improve the evidence base for adolescents presenting to primary care with dysmenorrhoea. It comprises three interlinked studies. Using the QResearch Database, the study population includes all female at birth participants aged 10-19 years any time between 1 January 2000 and 30 June 2021. We will undertake (1) a descriptive study documenting the prevalence of coded dysmenorrhoea in primary care, stratified by demographic variables, reported using descriptive statistics; (2) a prospective open cohort study following an index cohort of all adolescents recorded as attending primary care with dysmenorrhoea and a comparator cohort of five times as many who have not, to determine the HR for a diagnosis of endometriosis, adenomyosis, ongoing menstrual pain or subfertility (considered singly and in combination) anytime during the study period; and (3) a nested case-control study for adolescents diagnosed with endometriosis, using conditional logistic regression, to determine the OR for symptom(s) preceding this diagnosis., Ethics and Dissemination: The project has been independently peer reviewed and received ethics approval from the QResearch Scientific Board (reference OX46 under REC 18/EM/0400).In addition to publication in peer-reviewed academic journals, we will use the combined findings to generate a resource and infographic to support shared decision-making about dysmenorrhoea in community health settings. Additionally, the findings will be used to inform a subsequent qualitative study, exploring adolescents' experiences of menstrual pain., Competing Interests: Competing interests: KV has received research funding from Bayer Healthcare and honoraria for talks and consultancy fees from Bayer Healthcare, AbbVie and Eli Lilly. JHC is an unpaid director of QResearch, a not-for-profit organisation which is a partnership between the University of Oxford and EMIS Health who supply the QResearch database used for this work. JHC is a founder and shareholder of ClinRisk ltd and was its medical director until 31st May 2019. ClinRisk Ltd produces open and closed source software to implement clinical risk algorithms (outside the scope of this work) into clinical computer systemsThe remaining authors declare no relevant conflict of interest., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

Importance: Evidence indicates that preexisting neuropsychiatric conditions confer increased risks of severe outcomes from COVID-19 infection. It is unclear how this increased risk compares with risks associated with other severe acute respiratory infections (SARIs)., Objective: To determine whether preexisting diagnosis of and/or treatment for a neuropsychiatric condition is associated with severe outcomes from COVID-19 infection and other SARIs and whether any observed association is similar between the 2 outcomes., Design, Setting, and Participants: Prepandemic (2015-2020) and contemporary (2020-2021) longitudinal cohorts were derived from the QResearch database of English primary care records. Adjusted hazard ratios (HRs) with 99% CIs were estimated in April 2022 using flexible parametric survival models clustered by primary care clinic. This study included a population-based sample, including all adults in the database who had been registered with a primary care clinic for at least 1 year. Analysis of routinely collected primary care electronic medical records was performed., Exposures: Diagnosis of and/or medication for anxiety, mood, or psychotic disorders and diagnosis of dementia, depression, schizophrenia, or bipolar disorder., Main Outcomes and Measures: COVID-19-related mortality, or hospital or intensive care unit admission; SARI-related mortality, or hospital or intensive care unit admission., Results: The prepandemic cohort comprised 11 134 789 adults (223 569 SARI cases [2.0%]) with a median (IQR) age of 42 (29-58) years, of which 5 644 525 (50.7%) were female. The contemporary cohort comprised 8 388 956 adults (58 203 severe COVID-19 cases [0.7%]) with a median (IQR) age of 48 (34-63) years, of which 4 207 192 were male (50.2%). Diagnosis and/or treatment for neuropsychiatric conditions other than dementia was associated with an increased likelihood of a severe outcome from SARI (anxiety diagnosis: HR, 1.16; 99% CI, 1.13-1.18; psychotic disorder diagnosis and treatment: HR, 2.56; 99% CI, 2.40-2.72) and COVID-19 (anxiety diagnosis: HR, 1.16; 99% CI, 1.12-1.20; psychotic disorder treatment: HR, 2.37; 99% CI, 2.20-2.55). The effect estimate for severe outcome with dementia was higher for those with COVID-19 than SARI (HR, 2.85; 99% CI, 2.71-3.00 vs HR, 2.13; 99% CI, 2.07-2.19)., Conclusions and Relevance: In this longitudinal cohort study, UK patients with preexisting neuropsychiatric conditions and treatments were associated with similarly increased risks of severe outcome from COVID-19 infection and SARIs, except for dementia.

Objective: To evaluate the benefit of combining polygenic risk scores with the QCancer-10 (colorectal cancer) prediction model for non-genetic risk to identify people at highest risk of colorectal cancer., Design: Population based cohort study., Setting: Data from the UK Biobank study, collected between March 2006 and July 2010., Participants: 434 587 individuals with complete data for genetics and QCancer-10 predictions were included in the QCancer-10 plus polygenic risk score modelling and validation cohorts., Main Outcome Measures: Prediction of colorectal cancer diagnosis by genetic, non-genetic, and combined risk models. Using data from UK Biobank, six different polygenic risk scores for colorectal cancer were developed using LDpred2 polygenic risk score software, clumping, and thresholding approaches, and a model based on genome-wide significant polymorphisms. The top performing genome-wide polygenic risk score and the score containing genome-wide significant polymorphisms were combined with QCancer-10 and performance was compared with QCancer-10 alone. Case-control (logistic regression) and time-to-event (Cox proportional hazards) analyses were used to evaluate risk model performance in men and women., Results: Polygenic risk scores derived using the LDpred2 program performed best, with an odds ratio per standard deviation of 1.584 (95% confidence interval 1.536 to 1.633), and top age and sex adjusted C statistic of 0.733 (95% confidence interval 0.710 to 0.753) in logistic regression models in the validation cohort. Integrated QCancer-10 plus polygenic risk score models out-performed QCancer-10 alone. In men, the integrated LDpred2 model produced a C statistic of 0.730 (0.720 to 0.741) and explained variation of 28.2% (26.3 to 30.1), compared with 0.693 (0.682 to 0.704) and 21.0% (18.9 to 23.1) for QCancer-10 alone. In women, the C statistic for the integrated LDpred2 model was 0.687 (0.673 to 0.702) and explained variation was 21.0% (18.7 to 23.7), compared with 0.645 (0.631 to 0.659) and 12.4% (10.3 to 14.6) for QCancer-10 alone. In the top 20% of individuals at highest absolute risk, the sensitivity and specificity of the integrated LDpred2 models for predicting colorectal cancer diagnosis was 47.8% and 80.3% respectively in men, and 42.7% and 80.1% respectively in women, with increases in absolute risk in the top 5% of risk in men of 3.47-fold and in women of 2.77-fold compared with the median. Illustrative decision curve analysis indicated a small incremental improvement in net benefit with QCancer-10 plus polygenic risk score models compared with QCancer-10 alone., Conclusions: Integrating polygenic risk scores with QCancer-10 modestly improves risk prediction over use of QCancer-10 alone. Given that QCancer-10 data can be obtained relatively easily from health records, use of polygenic risk score in risk stratified population screening for colorectal cancer currently has no clear justification. The added benefit, cost effectiveness, and acceptability of polygenic risk scores should be carefully evaluated in a real life screening setting before implementation in the general population., Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: no support from any organisation for the submitted work other than those listed above; JH-C led the development of the QCancer-10 algorithm. JH-C is founder and shareholder of ClinRisk Ltd which supplies free open-source software for research purposes and also licenses other closed source software for a fee to implement risk prediction tools into NHS computer systems outside the submitted work and was its medical director until June 2019, JH-C is director of the QResearch database—a not-for-profit collaboration between University of Oxford and EMIS (commercial supplier of NHS computer systems), and is an adviser to the CMO in England on cancer screening; JEE has served on clinical advisory boards for Lumendi, Boston Scientific, and Paion, has served on the clinical advisory board and owns share options in Satisfai Health, reports speaker fees from Falk, Janssen and Medtronic, and serves on the ACPGBI/BSG guideline group for implementation faecal immunochemical testing for the detection of colorectal cancer in patients with symptoms suspicious of colorectal cancer., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Studies report an increased risk of self-harm or suicide in people prescribed mirtazapine compared with other antidepressants., Objectives: To compare the risk of serious self-harm in people prescribed mirtazapine versus other antidepressants as second-line treatments., Design and Setting: Cohort study using anonymised English primary care electronic health records, hospital admission data and mortality data with study window 1 January 2005 to 30 November 2018., Participants: 24 516 people diagnosed with depression, aged 18-99 years, initially prescribed a selective serotonin reuptake inhibitor (SSRI) and then prescribed mirtazapine, a different SSRI, amitriptyline or venlafaxine., Main Outcome Measures: Hospitalisation or death due to deliberate self-harm. Age-sex standardised rates were calculated and survival analyses were performed using inverse probability of treatment weighting to account for baseline covariates., Results: Standardised rates of serious self-harm ranged from 3.8/1000 person-years (amitriptyline) to 14.1/1000 person-years (mirtazapine). After weighting, the risk of serious self-harm did not differ significantly between the mirtazapine group and the SSRI or venlafaxine groups (HRs (95% CI) 1.18 (0.84 to 1.65) and 0.85 (0.51 to 1.41) respectively). The risk was significantly higher in the mirtazapine than the amitriptyline group (3.04 (1.36 to 6.79)) but was attenuated after adjusting for dose., Conclusions: There was no evidence for a difference in risk between mirtazapine and SSRIs or venlafaxine after accounting for baseline characteristics. The higher risk in the mirtazapine versus the amitriptyline group might reflect residual confounding if amitriptyline is avoided in people considered at risk of self-harm., Clinical Implications: Addressing baseline risk factors and careful monitoring might improve outcomes for people at risk of serious self-harm., Competing Interests: Competing interests: All authors have completed the ICMJE declaration of interest form and declare support from the National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, NIHR School for Primary Care Research, and NIHR MindTech MedTech Co-operative. JHC is a shareholder and former director of ClinRisk Ltd, outside of the submitted work. JHC is an unpaid director of QResearch which is an alternative database to CPRD run as a partnership between the University of Oxford and EMIS (commercial supplier of GP computer systems) outside the submitted work., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Objective: To systematically identify and compare the performance of prognostic models providing estimates of survival or recurrence of localized renal cell cancer (RCC) in patients treated with surgery with curative intent., Materials and Methods: We performed a systematic review (PROSPERO CRD42019162349). We searched Medline, EMBASE and the Cochrane Library from 1 January 2000 to 12 December 2019 to identify studies reporting the performance of one or more prognostic model(s) that predict recurrence-free survival (RFS), cancer-specific survival (CSS) or overall survival (OS) in patients who have undergone surgical resection for localized RCC. For each outcome we summarized the discrimination of each model using the C-statistic and performed multivariate random-effects meta-analysis of the logit transformed C-statistic to rank the models., Results: Of a total of 13 549 articles, 57 included data on the performance of 22 models in external populations. C-statistics ranged from 0.59 to 0.90. Several risk models were assessed in two or more external populations and had similarly high discriminative performance. For RFS, these were the Sorbellini, Karakiewicz, Leibovich and Kattan models, with the UCLA Integrated Staging System model also having similar performance in European/US populations. All had C-statistics ≥0.75 in at least half of the validations. For CSS, they the models with the highest discriminative performance in two or more external validation studies were the Zisman, Stage, Size, Grade and Necrosis (SSIGN), Karakiewicz, Leibovich and Sorbellini models (C-statistic ≥0.80 in at least half of the validations), and for OS they were the Leibovich, Karakiewicz, Sorbellini and SSIGN models. For all outcomes, the models based on clinical features at presentation alone (Cindolo and Yaycioglu) had consistently lower discrimination. Estimates of model calibration were only infrequently included but most underestimated survival., Conclusion: Several models had good discriminative ability, with there being no single 'best' model. The choice from these models for each setting should be informed by both the comparative performance and availability of factors included in the models. All would need recalibration if used to provide absolute survival estimates., (© 2022 The Authors. BJU International published by John Wiley & Sons Ltd on behalf of BJU International.)

Background and Research Aim: The incidence and mortality of liver cancer have been increasing in the UK in recent years. However, liver cancer is still under-studied. The Early Detection of Hepatocellular Liver Cancer (DeLIVER-QResearch) project aims to address the research gap and generate new knowledge to improve early detection and diagnosis of primary liver cancer from general practice and at the population level. There are three research objectives: (1) to understand the current epidemiology of primary liver cancer in England, (2) to identify and quantify the symptoms and comorbidities associated with liver cancer, and (3) to develop and validate prediction models for early detection of liver cancer suitable for implementation in clinical settings., Methods: This population-based study uses the QResearch® database (version 46) and includes adult patients aged 25-84 years old and without a diagnosis of liver cancer at the cohort entry (study period: 1 January 2008-30 June 2021). The team conducted a literature review (with additional clinical input) to inform the inclusion of variables for data extraction from the QResearch database. A wide range of statistical techniques will be used for the three research objectives, including descriptive statistics, multiple imputation for missing data, conditional logistic regression to investigate the association between the clinical features (symptoms and comorbidities) and the outcome, fractional polynomial terms to explore the non-linear relationship between continuous variables and the outcome, and Cox/competing risk regression for the prediction model. We have a specific focus on the 1-year, 5-year, and 10-year absolute risks of developing liver cancer, as risks at different time points have different clinical implications. The internal-external cross-validation approach will be used, and the discrimination and calibration of the prediction model will be evaluated., Discussion: The DeLIVER-QResearch project uses large-scale representative population-based data to address the most relevant research questions for early detection and diagnosis of primary liver cancer in England. This project has great potential to inform the national cancer strategic plan and yield substantial public and societal benefits., (© 2022. The Author(s).)

Background: Myocarditis is more common after severe acute respiratory syndrome coronavirus 2 infection than after COVID-19 vaccination, but the risks in younger people and after sequential vaccine doses are less certain., Methods: A self-controlled case series study of people ages 13 years or older vaccinated for COVID-19 in England between December 1, 2020, and December 15, 2021, evaluated the association between vaccination and myocarditis, stratified by age and sex. The incidence rate ratio and excess number of hospital admissions or deaths from myocarditis per million people were estimated for the 1 to 28 days after sequential doses of adenovirus (ChAdOx1) or mRNA-based (BNT162b2, mRNA-1273) vaccines, or after a positive SARS-CoV-2 test., Results: In 42 842 345 people receiving at least 1 dose of vaccine, 21 242 629 received 3 doses, and 5 934 153 had SARS-CoV-2 infection before or after vaccination. Myocarditis occurred in 2861 (0.007%) people, with 617 events 1 to 28 days after vaccination. Risk of myocarditis was increased in the 1 to 28 days after a first dose of ChAdOx1 (incidence rate ratio, 1.33 [95% CI, 1.09-1.62]) and a first, second, and booster dose of BNT162b2 (1.52 [95% CI, 1.24-1.85]; 1.57 [95% CI, 1.28-1.92], and 1.72 [95% CI, 1.33-2.22], respectively) but was lower than the risks after a positive SARS-CoV-2 test before or after vaccination (11.14 [95% CI, 8.64-14.36] and 5.97 [95% CI, 4.54-7.87], respectively). The risk of myocarditis was higher 1 to 28 days after a second dose of mRNA-1273 (11.76 [95% CI, 7.25-19.08]) and persisted after a booster dose (2.64 [95% CI, 1.25-5.58]). Associations were stronger in men younger than 40 years for all vaccines. In men younger than 40 years old, the number of excess myocarditis events per million people was higher after a second dose of mRNA-1273 than after a positive SARS-CoV-2 test (97 [95% CI, 91-99] versus 16 [95% CI, 12-18]). In women younger than 40 years, the number of excess events per million was similar after a second dose of mRNA-1273 and a positive test (7 [95% CI, 1-9] versus 8 [95% CI, 6-8])., Conclusions: Overall, the risk of myocarditis is greater after SARS-CoV-2 infection than after COVID-19 vaccination and remains modest after sequential doses including a booster dose of BNT162b2 mRNA vaccine. However, the risk of myocarditis after vaccination is higher in younger men, particularly after a second dose of the mRNA-1273 vaccine.

Background: Smoking is a risk factor for most respiratory infections, but it may protect against SARS-CoV-2 infection. The objective was to assess whether smoking and e-cigarette use were associated with severe COVID-19., Methods: This cohort ran from 24 January 2020 until 30 April 2020 at the height of the first wave of the SARS-CoV-2 epidemic in England. It comprised 7 869 534 people representative of the population of England with smoking status, demographic factors and diseases recorded by general practitioners in the medical records, which were linked to hospital and death data. The outcomes were COVID-19-associated hospitalization, intensive care unit (ICU) admission and death. The associations between smoking and the outcomes were assessed with Cox proportional hazards models, with sequential adjustment for confounding variables and indirect causal factors (body mass index and smoking-related disease)., Results: Compared with never smokers, people currently smoking were at lower risk of COVID-19 hospitalization, adjusted hazard ratios (HRs) were 0.64 (95% confidence intervals 0.60 to 0.69) for <10 cigarettes/day, 0.49 (0.41 to 0.59) for 10-19 cigarettes/day, and 0.61 (0.49 to 0.74) for ≥20 cigarettes/day. For ICU admission, the corresponding HRs were 0.31 (0.24 to 0.40), 0.15 (0.06 to 0.36), and 0.35 (0.17 to 0.74) and death were: 0.79 (0.70 to 0.89), 0.66 (0.48 to 0.90), and 0.77 (0.54 to 1.09) respectively. Former smokers were at higher risk of severe COVID-19: HRs: 1.07 (1.03 to 1.11) for hospitalization, 1.17 (1.04 to 1.31) for ICU admission, and 1.17 (1.10 to 1.24) for death. All-cause mortality was higher for current smoking than never smoking, HR 1.42 (1.36 to 1.48). Among e-cigarette users, the adjusted HR for e-cigarette use and hospitalization with COVID-19 was 1.06 (0.88 to 1.28), for ICU admission was 1.04 (0.57 to 1.89, and for death was 1.12 (0.81 to 1.55)., Conclusions: Current smoking was associated with a reduced risk of severe COVID-19 but the association with e-cigarette use was unclear. All-cause mortality remained higher despite this possible reduction in death from COVID-19 during an epidemic of SARS-CoV-2. Findings support investigating possible protective mechanisms of smoking for SARS-CoV-2 infection, including the ongoing trials of nicotine to treat COVID-19., (© The Author(s) 2022. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Background: HBV is the leading global cause of cirrhosis and primary liver cancer. However, the UK HBV population has not been well characterised, and estimates of UK HBV prevalence and/or incidence vary widely between sources. We summarised datasets that are available to represent UK CHB epidemiology, considering differences between sources, and discussing deficiencies in current estimates. Methods: We searched for estimates of CHB case numbers in the UK (incorporating incidence and/or prevalence-like data) across a range of available sources, including UK-wide reports from government bodies, publications from independent bodies (including medical charities and non-governmental organisations) and articles in peer-reviewed scientific journals to collate estimated positivity rates. An alternative proxy for population prevalence was obtained via the UK antenatal screening programme which achieves over 95% coverage of pregnant women. Results: We identified six CHB case number estimates, of which three reported information concerning population subgroups, including number of infected individuals across age, sex and ethnicity categories. Estimates among sources reporting prevalence varied from 0.27% to 0.73%, congruent with an estimated antenatal CHB prevalence of <0.5%.  Discussion : Estimates varied by sources of error, bias and missingness, data linkage, and substantial "blind spots" in consistent testing and registration of HBV diagnoses. The HBV burden in the UK is likely to be concentrated in vulnerable populations who may not be well represented in existing datasets including those experiencing socioeconomic deprivation, ethnic minorities, people experiencing homelessness and people born in high-prevalence countries. Together, these factors could lead to either under- or over-estimation of overall prevalence, and additional efforts are required to provide estimates that best reflect the whole population.  Multi-parameter evidence synthesis and back-calculation model methods similar to those used to generate estimates of HCV ad HIV population-wide prevalence may be applicable to HBV., Competing Interests: Competing interests: PCM is funded by Wellcome (grant ref 110110), by the Francis Crick Institute, and by University College London Hospitals NIHR BRC. CC’s doctoral project is funded by the Nuffield Department of Medicine, University of Oxford and by GlaxoSmithKline. EB is funded by the Oxford NIHR Biomedical Research Centre and is an NIHR Senior Investigator. The views expressed in this article are those of the author and not necessarily those of the NHS, the NIHR, or the Department of Health. PCM, EB and CC are supported by the DeLIVER program “The Early Detection of Hepatocellular Liver Cancer” project is funded by Cancer Research UK (Early Detection Programme Award, grant reference: C30358/A29725). EB, TW, CC and PCM acknowledge support from the NIHR Health Informatics Collaborative. JH-C is an unpaid director of QResearch, a not-for-profit organisation which is a partnership between the University of Oxford and EMIS Health that supplies the QResearch Database used for this work. JH-C is chair of the NERVTAG subgroup on risk stratification and a member of SAGE groups on data and ethnicity. JH-C is a founder and shareholder of ClinRisk and was its medical director until 31 May 2019., (Copyright: © 2022 Campbell C et al.)

Objective: To assess the risk of covid-19 death after infection with omicron BA.1 compared with delta (B.1.617.2)., Design: Retrospective cohort study., Setting: England, United Kingdom, from 1 December 2021 to 30 December 2021., Participants: 1 035 149 people aged 18-100 years who tested positive for SARS-CoV-2 under the national surveillance programme and had an infection identified as omicron BA.1 or delta compatible., Main Outcome Measures: The main outcome measure was covid-19 death as identified from death certification records. The exposure of interest was the SARS-CoV-2 variant identified from NHS Test and Trace PCR positive tests taken in the community (pillar 2) and analysed by Lighthouse laboratories. Cause specific Cox proportional hazard regression models (censoring non-covid-19 deaths) were adjusted for sex, age, vaccination status, previous infection, calendar time, ethnicity, index of multiple deprivation rank, household deprivation, university degree, keyworker status, country of birth, main language, region, disability, and comorbidities. Interactions between variant and sex, age, vaccination status, and comorbidities were also investigated., Results: The risk of covid-19 death was 66% lower (95% confidence interval 54% to 75%) for omicron BA.1 compared with delta after adjusting for a wide range of potential confounders. The reduction in the risk of covid-19 death for omicron compared with delta was more pronounced in people aged 18-59 years (number of deaths: delta=46, omicron=11; hazard ratio 0.14, 95% confidence interval 0.07 to 0.27) than in those aged ≥70 years (number of deaths: delta=113, omicron=135; hazard ratio 0.44, 95% confidence interval 0.32 to 0.61, P<0.0001). No evidence of a difference in risk was found between variant and number of comorbidities., Conclusions: The results support earlier studies showing a reduction in severity of infection with omicron BA.1 compared with delta in terms of hospital admission. This study extends the research to also show a reduction in the risk of covid-19 death for the omicron variant compared with the delta variant., Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; KK is chair of the ethnicity subgroup of the UK Scientific Advisory Group for Emergencies (SAGE) and is a member of SAGE. JH-C is chair of the NERVTAG risk stratification subgroup and is a member of SAGE., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: This study uses three linked datasets to provide an estimate of incidence of motor neuron disease (MND) in England from 1998 to 2019. Comparison is made to previous British studies. It examines age at diagnosis and ethnicity of those affected. Methods: The literature was searched for studies of MND incidence in Great Britain from 1995 to date. The QResearch and linked Hospital Episode Statistics and Death register databases were searched from 1998 to 2019 for cases of MND, and incidence calculated from 16.8 million adults and 112 million adult years of data. Results: We found 6437 adults with a diagnosis of MND giving an incidence of MND of 5.69/100,000 person years (95% CI 5.51-5.88); 6.57 (6.41-6.99) in men and 4.72 (4.49-4.97) in women when age-standardized to the 2011 UK population. The median age of diagnosis was 72 years. Peak incidence occurred in the 80-84 year age group in men and 75-79 in women. Age-standardized incidence was as high in Bangladeshi, Black Caribbean, Indian, other Asian and Pakistani people as in White people. Black African and Chinese people had a lower incidence. Conclusion: The use of three linked national datasets captured 33% more people than a primary care dataset alone. Patients were older than in previous studies and rates were high in all ethnic groups studied except Black African and Chinese people. We present the highest incidence of MND reported globally in the past 50 years. Methodological differences may in part explain differences with previous reports. The use of national datasets may have captured additional MND patients with serious comorbidities who have not seen a neurologist before death. A limitation of this approach is that unlike population registers, which minimize false positive diagnosis by neurologist review of each patient, we cannot review diagnosis for individuals as data are anonymized.

Objective: To investigate childhood, teenage and young adult cancer diagnostic pathways during the first wave of the COVID-19 pandemic in England., Design: Population-based cohort study., Setting and Participants: QResearch, a nationally representative primary care database, linked to hospital admission, mortality and cancer registry data, was used to identify childhood, teenage and young adult cancers (0-24 years) diagnosed between 1 January 2017 and 15 August 2020., Main Outcomes: Main outcomes of interest were: (1) number of incident cancer diagnoses per month, (2) diagnostic, treatment time intervals and (3) cancer-related intensive care admissions., Results: 2607 childhood, teenage and young adult cancers were diagnosed from 1 January 2017 to 15 August 2020; 380 were diagnosed during the pandemic period. Overall, 17% (95% CI -28.0% to -4.0%) reduction in the incidence rate ratio of cancers was observed during the pandemic. Specific decreases were seen for central nervous system tumour (-38% (95% CI -52% to -21%)) and lymphoma (-28% (95% CI -45% to -5%)) diagnoses. Additionally, childhood cancers diagnosed during the pandemic were significantly more likely to have intensive care admissions (adjusted OR 2.2 (95% CI 1.33 to 3.47)). Median time-to-diagnosis did not significantly differ across periods (+4.5 days (95% CI -20.5 to +29.5)), while median time-to-treatment was shorter during the pandemic (-0.7 days (95% CI -1.1 to -0.3))., Conclusions: Collectively, our findings of a significant reduction in cancer diagnoses and increase in intensive care admissions provide initial insight into the changes that occurred to childhood, teenage and young adult cancer diagnostic pathways during the first wave of the pandemic., Competing Interests: Competing interests: JH-C reports grants from the National Institute for Health Research Biomedical Research Centre, Oxford, grants from John Fell Oxford University Press Research Fund, grants from Cancer Research UK (CR-UK) (grant number C5255/A18085), through the Cancer Research UK Oxford Centre, grants from the Oxford Wellcome Institutional Strategic Support Fund (204826/Z/16/Z), during the conduct of the study. JH-C is an unpaid director of QResearch, a not-for-profit organisation which is a partnership between the University of Oxford and EMIS Health that supplies the QResearch Database used for this work. JH-C is chair of the NERVTAG subgroup on risk stratification and a member of SAGE groups on data and ethnicity. JH-C is a founder and shareholder of ClinRisk and was its medical director until 31 May 2019. ClinRisk produces open and closed source software to implement clinical risk algorithms (outside this work) into clinical computer systems. DS, AH and JO declare no conflicts of interest., (© Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: A high BMI has been associated with a reduced immune response to vaccination against influenza. We aimed to investigate the association between BMI and COVID-19 vaccine uptake, vaccine effectiveness, and risk of severe COVID-19 outcomes after vaccination by using a large, representative population-based cohort from England., Methods: In this population-based cohort study, we used the QResearch database of general practice records and included patients aged 18 years or older who were registered at a practice that was part of the database in England between Dec 8, 2020 (date of the first vaccination in the UK), to Nov 17, 2021, with available data on BMI. Uptake was calculated as the proportion of people with zero, one, two, or three doses of the vaccine across BMI categories. Effectiveness was assessed through a nested matched case-control design to estimate odds ratios (OR) for severe COVID-19 outcomes (ie, admission to hospital or death) in people who had been vaccinated versus those who had not, considering vaccine dose and time periods since vaccination. Vaccine effectiveness against infection with SARS-CoV-2 was also investigated. Multivariable Cox proportional hazard models estimated the risk of severe COVID-19 outcomes associated with BMI (reference BMI 23 kg/m 2 ) after vaccination., Findings: Among 9 171 524 participants (mean age 52 [SD 19] years; BMI 26·7 [5·6] kg/m 2 ), 566 461 tested positive for SARS-CoV-2 during follow-up, of whom 32 808 were admitted to hospital and 14 389 died. Of the total study sample, 19·2% (1 758 689) were unvaccinated, 3·1% (287 246) had one vaccine dose, 52·6% (4 828 327) had two doses, and 25·0% (2 297 262) had three doses. In people aged 40 years and older, uptake of two or three vaccine doses was more than 80% among people with overweight or obesity, which was slightly lower in people with underweight (70-83%). Although significant heterogeneity was found across BMI groups, protection against severe COVID-19 disease (comparing people who were vaccinated vs those who were not) was high after 14 days or more from the second dose for hospital admission (underweight: OR 0·51 [95% CI 0·41-0·63]; healthy weight: 0·34 [0·32-0·36]; overweight: 0·32 [0·30-0·34]; and obesity: 0·32 [0·30-0·34]) and death (underweight: 0·60 [0·36-0·98]; healthy weight: 0·39 [0·33-0·47]; overweight: 0·30 [0·25-0·35]; and obesity: 0·26 [0·22-0·30]). In the vaccinated cohort, there were significant linear associations between BMI and COVID-19 hospitalisation and death after the first dose, and J-shaped associations after the second dose., Interpretation: Using BMI categories, there is evidence of protection against severe COVID-19 in people with overweight or obesity who have been vaccinated, which was of a similar magnitude to that of people of healthy weight. Vaccine effectiveness was slightly lower in people with underweight, in whom vaccine uptake was also the lowest for all ages. In the vaccinated cohort, there were increased risks of severe COVID-19 outcomes for people with underweight or obesity compared with the vaccinated population with a healthy weight. These results suggest the need for targeted efforts to increase uptake in people with low BMI (<18·5 kg/m 2 ), in whom uptake is lower and vaccine effectiveness seems to be reduced. Strategies to achieve and maintain a healthy weight should be prioritised at the population level, which could help reduce the burden of COVID-19 disease., Funding: UK Research and Innovation and National Institute for Health Research Oxford Biomedical Research Centre., Competing Interests: Declaration of interests QResearch is a registered trademark of Egton Medical Information Systems and the University of Nottingham. PA spoke at a symposium at the Royal College of General Practitioners annual conference on interventions for weight loss that was funded by Novo Nordisk but received no personal payments. JH-C received personal fees and other support from ClinRisk (until 2019) outside the submitted work and is an unpaid director of QResearch. MG, NMA, CP, MP, SD, MS-H, CC, and SAJ declare no competing interests. AS is a member of the Scottish Government COVID-19 Chief Medical Officers Advisory Group and its Standing Committee on Pandemics; a member of NERVTAG's Risk Stratification Subgroup; and a member of AstraZeneca's Thrombotic Thrombocytopenic Taskforce. KK is a member of Chair of the Ethnicity Subgroup of the UK Scientific Advisory Group for Emergencies (SAGE) and a member of SAGE., (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Importance: Individuals surviving severe COVID-19 may be at increased risk of neuropsychiatric sequelae. Robust assessment of these risks may help improve clinical understanding of the post-COVID syndrome, aid clinical care during the ongoing pandemic, and inform postpandemic planning., Objective: To quantify the risks of new-onset neuropsychiatric conditions and new neuropsychiatric medication prescriptions after discharge from a COVID-19-related hospitalization, and to compare these with risks after discharge from hospitalization for other severe acute respiratory infections (SARI) during the COVID-19 pandemic., Design, Setting, and Participants: In this cohort study, adults (≥18 years of age) were identified from QResearch primary care and linked electronic health record databases, including national SARS-CoV-2 testing, hospital episode statistics, intensive care admissions data, and mortality registers in England, from January 24, 2020, to July 7, 2021., Exposures: COVID-19-related or SARI-related hospital admission (including intensive care admission)., Main Outcomes and Measures: New-onset diagnoses of neuropsychiatric conditions (anxiety, dementia, psychosis, depression, bipolar disorder) or first prescription for relevant medications (antidepressants, hypnotics/anxiolytics, antipsychotics) during 12 months of follow-up from hospital discharge. Maximally adjusted hazard ratios (HR) with 95% CIs were estimated using flexible parametric survival models., Results: In this cohort study of data from 8.38 million adults (4.18 million women, 4.20 million men; mean [SD] age 49.18 [18.45] years); 16 679 (0.02%) survived a hospital admission for SARI, and 32 525 (0.03%) survived a hospital admission for COVID-19. Compared with the remaining population, survivors of SARI and COVID-19 hospitalization had higher risks of subsequent neuropsychiatric diagnoses. For example, the HR for anxiety in survivors of SARI was 1.86 (95% CI, 1.56-2.21) and for survivors of COVID-19 infection was 2.36 (95% CI, 2.03-2.74); the HR for dementia for survivors of SARI was 2.55 (95% CI, 2.17-3.00) and for survivors of COVID-19 infection was 2.63 (95% CI, 2.21-3.14). Similar findings were observed for all medications analyzed; for example, the HR for first prescriptions of antidepressants in survivors of SARI was 2.55 (95% CI, 2.24-2.90) and for survivors of COVID-19 infection was 3.24 (95% CI, 2.91-3.61). There were no significant differences observed when directly comparing the COVID-19 group with the SARI group apart from a lower risk of antipsychotic prescriptions in the former (HR, 0.80; 95% CI, 0.69-0.92)., Conclusions and Relevance: In this cohort study, the neuropsychiatric sequelae of severe COVID-19 infection were found to be similar to those for other SARI. This finding may inform postdischarge support for people surviving SARI.

Introduction: The QCOVID algorithm is a risk prediction tool for infection and subsequent hospitalisation/death due to SARS-CoV-2. At the time of writing, it is being used in important policy-making decisions by the UK and devolved governments for combatting the COVID-19 pandemic, including deliberations on shielding and vaccine prioritisation. There are four statistical validations exercises currently planned for the QCOVID algorithm, using data pertaining to England, Northern Ireland, Scotland and Wales, respectively. This paper presents a common procedure for conducting and reporting on validation exercises for the QCOVID algorithm., Methods and Analysis: We will use open, retrospective cohort studies to assess the performance of the QCOVID risk prediction tool in each of the four UK nations. Linked datasets comprising of primary and secondary care records, virological testing data and death registrations will be assembled in trusted research environments in England, Scotland, Northern Ireland and Wales. We will seek to have population level coverage as far as possible within each nation. The following performance metrics will be calculated by strata: Harrell's C, Brier Score, R 2 and Royston's D., Ethics and Dissemination: Approvals have been obtained from relevant ethics bodies in each UK nation. Findings will be made available to national policy-makers, presented at conferences and published in peer-reviewed journal., Competing Interests: Competing interests: AS reports grants from NIHR, grants from MRC, and grants from HRR UK, during the conduct of the study. JL and RL report grants from UKRI Medical Research Council, during the conduct of the study. JH-C reports grants from John Fell Oxford University Press Research Fund, grants from Cancer Research UK (CR-UK) grant number C5255/A18085, through the Cancer Research UK Oxford Centre, grants from the Oxford Wellcome Institutional Strategic Support Fund (204826/Z/16/Z), grants from NIHR, during the conduct of the study; personal fees and other from ClinRisk, outside the submitted work; and JH-C is an unpaid director of QResearch, a not-for-profit organisation which is a partnership between the University of Oxford and EMIS Health who supply the QResearch database used for this work. Carol Coupland reports personal fees from ClinRisk, outside the submitted work. JH-C, AS and CC were members of the research team involved in the development of the QCOVID risk prediction algorithm. All other authors report no conflict of interest, (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Background: The QCovid algorithm is a risk prediction tool that can be used to stratify individuals by risk of COVID-19 hospitalisation and mortality. Version 1 of the algorithm was trained using data covering 10.5 million patients in England in the period 24 January 2020 to 30 April 2020. We carried out an external validation of version 1 of the QCovid algorithm in Scotland., Methods: We established a national COVID-19 data platform using individual level data for the population of Scotland (5.4 million residents). Primary care data were linked to reverse-transcription PCR (RT-PCR) virology testing, hospitalisation and mortality data. We assessed the performance of the QCovid algorithm in predicting COVID-19 hospitalisations and deaths in our dataset for two time periods matching the original study: 1 March 2020 to 30 April 2020, and 1 May 2020 to 30 June 2020., Results: Our dataset comprised 5 384 819 individuals, representing 99% of the estimated population (5 463 300) resident in Scotland in 2020. The algorithm showed good calibration in the first period, but systematic overestimation of risk in the second period, prior to temporal recalibration. Harrell's C for deaths in females and males in the first period was 0.95 (95% CI 0.94 to 0.95) and 0.93 (95% CI 0.92 to 0.93), respectively. Harrell's C for hospitalisations in females and males in the first period was 0.81 (95% CI 0.80 to 0.82) and 0.82 (95% CI 0.81 to 0.82), respectively., Conclusions: Version 1 of the QCovid algorithm showed high levels of discrimination in predicting the risk of COVID-19 hospitalisations and deaths in adults resident in Scotland for the original two time periods studied, but is likely to need ongoing recalibration prospectively., Competing Interests: Competing interests: JH-C reports grants from MRC, grants from Wellcome Trust, grants from NIHR, during the conduct of the study; other from ClinRisk, outside the submitted work. AS reports grants from NIHR, grants from MRC, grants from HDR UK, during the conduct of the study., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Guidelines recommend that GPs give patients lifestyle advice to manage hypertension and diabetes. Increasing evidence shows that this is an effective and practical treatment for these conditions, but it is unclear whether GPs offer this support., Aim: To investigate trends in the percentage of patients with hypertension/diabetes receiving lifestyle advice versus medication., Design and Setting: This was a trend analysis of self-reported data from the annual Health Survey for England (HSE) (2003-2017) and GP-recorded data from the QResearch database (2002-2016)., Method: The percentage of patients with hypertension or diabetes who received lifestyle advice or medication was calculated in each year. Associations between likelihood of receiving lifestyle advice and characteristics were assessed using multivariable logistic regression., Results: The percentage of patients receiving lifestyle advice was consistently lower than those receiving medication in both self-reported and medical records. There was consistent evidence of increasing trends in the percentage of patients with hypertension receiving lifestyle advice (HSE 13.8% to 20.1%; P trend <0.001; QResearch 11.0% to 22.7%; P trend <0.001). For diabetes, there was a non-significant decline in self-reported receipt of lifestyle advice (45.0% to 27.9%; P trend = 0.111) and a significant increase in medically recorded delivery of this advice (20.7% to 40.5%; P trend <0.001). Patients with hypertension who were overweight or obese were more likely to receive lifestyle advice than those of a healthy weight, whereas the opposite was true for diabetes., Conclusion: Only a minority of patients with diabetes or hypertension report receiving lifestyle advice or have this recorded in their medical records. Interventions beyond guidelines are needed to increase the delivery of behavioural interventions to treat these conditions., (© The Authors.)

Introduction: Breast cancer is the most common cancer and the leading cause of cancer-related death in women worldwide. Risk prediction models may be useful to guide risk-reducing interventions (such as pharmacological agents) in women at increased risk or inform screening strategies for early detection methods such as screening., Methods and Analysis: The study will use data for women aged 20-90 years between 2000 and 2020 from QResearch linked at the individual level to hospital episodes, cancer registry and death registry data. It will evaluate a set of modelling approaches to predict the risk of developing breast cancer within the next 10 years, the 'combined' risk of developing a breast cancer and then dying from it within 10 years, and the risk of breast cancer mortality within 10 years of diagnosis. Cox proportional hazards, competing risks, random survival forest, deep learning and XGBoost models will be explored. Models will be developed on the entire dataset, with 'apparent' performance reported, and internal-external cross-validation used to assess performance and geographical and temporal transportability (two 10-year time periods). Random effects meta-analysis will pool discrimination and calibration metric estimates from individual geographical units obtained from internal-external cross-validation. We will then externally validate the models in an independent dataset. Evaluation of performance heterogeneity will be conducted throughout, such as exploring performance across ethnic groups., Ethics and Dissemination: Ethics approval was granted by the QResearch scientific committee (reference number REC 18/EM/0400: OX129). The results will be written up for submission to peer-reviewed journals., Competing Interests: Competing interests: JH-C is an unpaid director of QResearch (a not-for-profit organisation which is a partnership between the University of Oxford and EMIS Health who supply the QResearch database) and is a founder and shareholder of ClinRisk and was its medical director until 31 May 2019 (ClinRisk produces open and closed source software to implement clinical risk algorithms (including a breast cancer prediction algorithm) into clinical computer systems., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Apart from high-risk scenarios such as the presence of highly penetrant genetic mutations, breast screening typically comprises mammography or tomosynthesis strategies defined by age. However, age-based screening ignores the range of breast cancer risks that individual women may possess and is antithetical to the ambitions of personalised early detection. Whilst screening mammography reduces breast cancer mortality, this is at the risk of potentially significant harms including overdiagnosis with overtreatment, and psychological morbidity associated with false positives. In risk-stratified screening, individualised risk assessment may inform screening intensity/interval, starting age, imaging modality used, or even decisions not to screen. However, clear evidence for its benefits and harms needs to be established. In this scoping review, the authors summarise the established and emerging evidence regarding several critical dependencies for successful risk-stratified breast screening: risk prediction model performance, epidemiological studies, retrospective clinical evaluations, health economic evaluations and qualitative research on feasibility and acceptability. Family history, breast density or reproductive factors are not on their own suitable for precisely estimating risk and risk prediction models increasingly incorporate combinations of demographic, clinical, genetic and imaging-related parameters. Clinical evaluations of risk-stratified screening are currently limited. Epidemiological evidence is sparse, and randomised trials only began in recent years., (© 2021. The Author(s).)

Introduction: COVID-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., Objectives: To validate externally the QCOVID risk prediction algorithm that predicts mortality outcomes from COVID-19 in the adult population of Wales, UK., Methods: We 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 24 th January 2020, registered with a SAIL-providing general practice, and followed-up to death or study end (28 th 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 R 2 values (explained variation), Brier scores, and measures of discrimination and calibration were calculated for two periods (24 th January-30 th April 2020 and 1 st May-28 th July 2020) to assess algorithm performance., Results: 1,956,760 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., Conclusions: The QCOVID algorithm developed in England can be used for public health risk management for the adult Welsh population., Competing Interests: Conflicts of interest: AS is a member of the Scottish Government’s COVID-19 Chief Medical Officer’s Advisory Group and its Standing Committee on Pandemics; he is also a member of NERVTAG’s Risk Stratification Subgroup. KK is member of NERVTAG subgroup and member of the Scientific Advisory Group for Emergencies (SAGE). JHC reports grants from National Institute for Health Research (NIHR) Biomedical Research Centre, Oxford, grants from John Fell Oxford University Press Research Fund, grants from Cancer Research UK (CR-UK) grant number C5255/A18085, through the Cancer Research UK Oxford Centre, grants from the Oxford Wellcome Institutional Strategic Support Fund (204826/Z/16/Z) and other research councils, during the conduct of the study. JHC is an unpaid director of QResearch, a not-for-profit organisation which is a partnership between the University of Oxford and EMIS Health who supply the QResearch database used for this work. JHC is a founder and shareholder of ClinRisk ltd and was its medical director until 31st May 2019. ClinRisk Ltd produces open and closed source software to implement clinical risk algorithms (outside this work) into clinical computer systems. JHC is chair of the NERVTAG risk stratification subgroup and a member of SAGE COVID-19 groups and the NHS group advising on prioritisation of use of monoclonal antibodies in COVID-19 infection. RAL is a member of the Welsh Government COVID-19 Technical Advisory Group.

Background: Studies have reported an increased risk of mortality among people prescribed mirtazapine compared to other antidepressants. The study aimed to compare all-cause and cause-specific mortality between adults prescribed mirtazapine or other second-line antidepressants., Methods: This cohort study used English primary care electronic medical records, hospital admission records, and mortality data from the Clinical Practice Research Datalink (CPRD), for the period 01 January 2005 to 30 November 2018. It included people aged 18-99 years with depression first prescribed a selective serotonin reuptake inhibitor (SSRI) and then prescribed mirtazapine (5081), a different SSRI (15,032), amitriptyline (3905), or venlafaxine (1580). Follow-up was from starting to stopping the second antidepressant, with a 6-month wash-out window, censoring at the end of CPRD follow-up or 30 November 2018. Age-sex standardised rates of all-cause mortality and death due to circulatory system disease, cancer, or respiratory system disease were calculated. Survival analyses were performed, accounting for baseline characteristics using inverse probability of treatment weighting., Results: The cohort contained 25,598 people (median age 41 years). The mirtazapine group had the highest standardised mortality rate, with an additional 7.8 (95% confidence interval (CI) 5.9-9.7) deaths/1000 person-years compared to the SSRI group. Within 2 years of follow-up, the risk of all-cause mortality was statistically significantly higher in the mirtazapine group than in the SSRI group (weighted hazard ratio (HR) 1.62, 95% CI 1.28-2.06). No significant difference was found between the mirtazapine group and the amitriptyline (HR 1.18, 95% CI 0.85-1.63) or venlafaxine (HR 1.11, 95% CI 0.60-2.05) groups. After 2 years, the risk was significantly higher in the mirtazapine group compared to the SSRI (HR 1.51, 95% CI 1.04-2.19), amitriptyline (HR 2.59, 95% CI 1.38-4.86), and venlafaxine (HR 2.35, 95% CI 1.02-5.44) groups. The risks of death due to cancer (HR 1.74, 95% CI 1.06-2.85) and respiratory system disease (HR 1.72, 95% CI 1.07-2.77) were significantly higher in the mirtazapine than in the SSRI group., Conclusions: Mortality was higher in people prescribed mirtazapine than people prescribed a second SSRI, possibly reflecting residual differences in other risk factors between the groups. Identifying these potential health risks when prescribing mirtazapine may help reduce the risk of mortality., (© 2022. The Author(s).)

Although myocarditis and pericarditis were not observed as adverse events in coronavirus disease 2019 (COVID-19) vaccine trials, there have been numerous reports of suspected cases following vaccination in the general population. We undertook a self-controlled case series study of people aged 16 or older vaccinated for COVID-19 in England between 1 December 2020 and 24 August 2021 to investigate hospital admission or death from myocarditis, pericarditis and cardiac arrhythmias in the 1-28 days following adenovirus (ChAdOx1, n = 20,615,911) or messenger RNA-based (BNT162b2, n = 16,993,389; mRNA-1273, n = 1,006,191) vaccines or a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive test (n = 3,028,867). We found increased risks of myocarditis associated with the first dose of ChAdOx1 and BNT162b2 vaccines and the first and second doses of the mRNA-1273 vaccine over the 1-28 days postvaccination period, and after a SARS-CoV-2 positive test. We estimated an extra two (95% confidence interval (CI) 0, 3), one (95% CI 0, 2) and six (95% CI 2, 8) myocarditis events per 1 million people vaccinated with ChAdOx1, BNT162b2 and mRNA-1273, respectively, in the 28 days following a first dose and an extra ten (95% CI 7, 11) myocarditis events per 1 million vaccinated in the 28 days after a second dose of mRNA-1273. This compares with an extra 40 (95% CI 38, 41) myocarditis events per 1 million patients in the 28 days following a SARS-CoV-2 positive test. We also observed increased risks of pericarditis and cardiac arrhythmias following a positive SARS-CoV-2 test. Similar associations were not observed with any of the COVID-19 vaccines, apart from an increased risk of arrhythmia following a second dose of mRNA-1273. Subgroup analyses by age showed the increased risk of myocarditis associated with the two mRNA vaccines was present only in those younger than 40., (© 2021. The Author(s).)

Background: Conflicting evidence has emerged regarding the relevance of smoking on risk of COVID-19 and its severity., Methods: We undertook large-scale observational and Mendelian randomisation (MR) analyses using UK Biobank. Most recent smoking status was determined from primary care records (70.8%) and UK Biobank questionnaire data (29.2%). COVID-19 outcomes were derived from Public Health England SARS-CoV-2 testing data, hospital admissions data, and death certificates (until 18 August 2020). Logistic regression was used to estimate associations between smoking status and confirmed SARS-CoV-2 infection, COVID-19-related hospitalisation, and COVID-19-related death. Inverse variance-weighted MR analyses using established genetic instruments for smoking initiation and smoking heaviness were undertaken (reported per SD increase)., Results: There were 421 469 eligible participants, 1649 confirmed infections, 968 COVID-19-related hospitalisations and 444 COVID-19-related deaths. Compared with never-smokers, current smokers had higher risks of hospitalisation (OR 1.80, 95% CI 1.26 to 2.29) and mortality (smoking 1-9/day: OR 2.14, 95% CI 0.87 to 5.24; 10-19/day: OR 5.91, 95% CI 3.66 to 9.54; 20+/day: OR 6.11, 95% CI 3.59 to 10.42). In MR analyses of 281 105 White British participants, genetically predicted propensity to initiate smoking was associated with higher risks of infection (OR 1.45, 95% CI 1.10 to 1.91) and hospitalisation (OR 1.60, 95% CI 1.13 to 2.27). Genetically predicted higher number of cigarettes smoked per day was associated with higher risks of all outcomes (infection OR 2.51, 95% CI 1.20 to 5.24; hospitalisation OR 5.08, 95% CI 2.04 to 12.66; and death OR 10.02, 95% CI 2.53 to 39.72)., Interpretation: Congruent results from two analytical approaches support a causal effect of smoking on risk of severe COVID-19., Competing Interests: Competing interests: PST reports personal fees from AstraZeneca, and personal fees from Duke-NUS, outside the submitted work. CACC reports receiving personal fees from ClinRisk, outside this work. MRM reports grants from Pfizer and Rusan, outside the submitted work. JHC is an unpaid director of QResearch, a not-for-profit organisation which is a partnership between the University of Oxford and EMIS Health. JHC is a founder and shareholder of ClinRisk Ltd and was its medical director until 31 May 2019; ClinRisk produces open and closed source software to implement clinical risk algorithms (outside this work) into clinical computer systems. AvE and JCH work at the Clinical Trial Service Unit and Epidemiological Studies Unit, which receives research grants from industry that are governed by University of Oxford contracts that protect its independence, and has a staff policy of not taking personal payments from industry; further details can be found at https://www.ndph.ox.ac.uk/files/about/ndph-independence-of-research-policy-jun-20.pdf. AKC, HMS, NL and PA have no conflicts to disclose., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: To estimate the association between untreated, community acquired, respiratory tract infections and bleeding in oral anticoagulant users., Design: Self-controlled case series., Setting: General practices in England contributing data to the Clinical Practice Research Datalink GOLD., Participants: 1208 adult users of warfarin or direct oral anticoagulants with a general practice or hospital admission record of a bleeding event between January 2010 and December 2019, and a general practice record of a consultation for a community acquired respiratory tract infection for which immediate antibiotics were not prescribed (that is, untreated)., Main Outcome Measures: Relative incidence of major bleeding and clinically relevant non-major bleeding in the 0-14 days after an untreated respiratory tract infection, compared to unexposed time periods., Results: Of 1208 study participants, 58% (n=701) were male, median age at time of first bleed was 79 years (interquartile range 72-85), with a median observation period of 2.4 years (interquartile range 1.3-3.8). 292 major bleeds occurred during unexposed time periods and 41 in the 0-14 days after consultation for a respiratory tract infection. 1003 clinically relevant non-major bleeds occurred during unexposed time periods and 81 in the 0-14 days after consultation for a respiratory tract infection. After adjustment for age, season, and calendar year, the relative incidence of major bleeding (incidence rate ratio 2.68, 95% confidence interval 1.83 to 3.93) and clinically relevant non-major bleeding (2.32, 1.82 to 2.94) increased in the 0-14 days after an untreated respiratory tract infection. Findings were robust to several sensitivity analyses and did not differ by sex or type of oral anticoagulant., Conclusions: This study observed a greater than twofold increase in the risk of bleeding during the 0-14 days after an untreated respiratory tract infection. These findings have potential implications for how patients and clinicians manage oral anticoagulant use during an acute intercurrent illness and warrant further investigation into the potential risks and how they might be mitigated., Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from the Health and Care Research Wales for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years, no other relationships or activities that could appear to have influenced the submitted work., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. No commercial re-use. See rights and permissions. Published by BMJ.)

Emerging reports of rare neurological complications associated with COVID-19 infection and vaccinations are leading to regulatory, clinical and public health concerns. We undertook a self-controlled case series study to investigate hospital admissions from neurological complications in the 28 days after a first dose of ChAdOx1nCoV-19 (n = 20,417,752) or BNT162b2 (n = 12,134,782), and after a SARS-CoV-2-positive test (n = 2,005,280). There was an increased risk of Guillain-Barré syndrome (incidence rate ratio (IRR), 2.90; 95% confidence interval (CI): 2.15-3.92 at 15-21 days after vaccination) and Bell's palsy (IRR, 1.29; 95% CI: 1.08-1.56 at 15-21 days) with ChAdOx1nCoV-19. There was an increased risk of hemorrhagic stroke (IRR, 1.38; 95% CI: 1.12-1.71 at 15-21 days) with BNT162b2. An independent Scottish cohort provided further support for the association between ChAdOx1nCoV and Guillain-Barré syndrome (IRR, 2.32; 95% CI: 1.08-5.02 at 1-28 days). There was a substantially higher risk of all neurological outcomes in the 28 days after a positive SARS-CoV-2 test including Guillain-Barré syndrome (IRR, 5.25; 95% CI: 3.00-9.18). Overall, we estimated 38 excess cases of Guillain-Barré syndrome per 10 million people receiving ChAdOx1nCoV-19 and 145 excess cases per 10 million people after a positive SARS-CoV-2 test. In summary, although we find an increased risk of neurological complications in those who received COVID-19 vaccines, the risk of these complications is greater following a positive SARS-CoV-2 test., (© 2021. The Author(s).)

Background: Oral prednisolone is the mainstay treatment for bullous pemphigoid, an autoimmune blistering skin disorder affecting older people. Treatment with moderate-to-high doses is often initiated in secondary care, but then continued in primary care., Aim: To describe long-term oral prednisolone prescribing in UK primary care for adults with bullous pemphigoid from 1998 to 2017., Design and Setting: A prospective cohort study using routinely collected data from the Clinical Practice Research Datalink, a primary care database containing the healthcare records for over 17 million people in the UK., Method: Oral prednisolone exposure was characterised in terms of the proportion of individuals with incident bullous pemphigoid prescribed oral prednisolone following their diagnosis, and the duration and dose of prednisolone., Results: In total, 2312 (69.6%) of 3322 people with bullous pemphigoid were prescribed oral prednisolone in primary care. The median duration of exposure was 10.6 months (interquartile range [IQR] 3.4-24.0). Of prednisolone users, 71.5% were continuously exposed for >3 months, 39.7% for >1 year, 14.7% for >3 years, 5.0% for >5 years, and 1.7% for >10 years. The median cumulative dose was 2974 mg (IQR 1059-6456). Maximum daily doses were ≥10 mg/day in 74.4% of prednisolone users, ≥20 mg/day in 40.7%, ≥30 mg/day in 18.2%, ≥40 mg/day in 6.6%, ≥50 mg/day in 3.8%, and ≥60 mg/day in 1.9%., Conclusion: A high proportion of people with incident bullous pemphigoid are treated with oral prednisolone in UK primary care. Action is required by primary and second care services to encourage use of steroid-sparing alternatives and, where switching is not possible, ensure prophylactic treatments and proactive monitoring of potential side effects are in place., (© The Authors.)

Background: A more transmissible variant of SARS-CoV-2, the variant of concern 202012/01 or lineage B.1.1.7, has emerged in the UK. We aimed to estimate the risk of critical care admission, mortality in patients who are critically ill, and overall mortality associated with lineage B.1.1.7 compared with non-B.1.1.7. We also compared clinical outcomes between these two groups., Methods: For this observational cohort study, we linked large primary care (QResearch), national critical care (Intensive Care National Audit & Research Centre Case Mix Programme), and national COVID-19 testing (Public Health England) databases. We used SARS-CoV-2 positive samples with S-gene molecular diagnostic assay failure (SGTF) as a proxy for the presence of lineage B.1.1.7. We extracted two cohorts from the data: the primary care cohort, comprising patients in primary care with a positive community COVID-19 test reported between Nov 1, 2020, and Jan 26, 2021, and known SGTF status; and the critical care cohort, comprising patients admitted for critical care with a positive community COVID-19 test reported between Nov 1, 2020, and Jan 27, 2021, and known SGTF status. We explored the associations between SARS-CoV-2 infection with and without lineage B.1.1.7 and admission to a critical care unit (CCU), 28-day mortality, and 28-day mortality following CCU admission. We used Royston-Parmar models adjusted for age, sex, geographical region, other sociodemographic factors (deprivation index, ethnicity, household housing category, and smoking status for the primary care cohort; and ethnicity, body-mass index, deprivation index, and dependency before admission to acute hospital for the CCU cohort), and comorbidities (asthma, chronic obstructive pulmonary disease, type 1 and 2 diabetes, and hypertension for the primary care cohort; and cardiovascular disease, respiratory disease, metastatic disease, and immunocompromised conditions for the CCU cohort). We reported information on types and duration of organ support for the B.1.1.7 and non-B.1.1.7 groups., Findings: The primary care cohort included 198 420 patients with SARS-CoV-2 infection. Of these, 117 926 (59·4%) had lineage B.1.1.7, 836 (0·4%) were admitted to CCU, and 899 (0·4%) died within 28 days. The critical care cohort included 4272 patients admitted to CCU. Of these, 2685 (62·8%) had lineage B.1.1.7 and 662 (15·5%) died at the end of critical care. In the primary care cohort, we estimated adjusted hazard ratios (HRs) of 2·15 (95% CI 1·75-2·65) for CCU admission and 1·65 (1·36-2·01) for 28-day mortality for patients with lineage B.1.1.7 compared with the non-B.1.1.7 group. The adjusted HR for mortality in critical care, estimated with the critical care cohort, was 0·91 (0·76-1·09) for patients with lineage B.1.1.7 compared with those with non-B.1.1.7 infection., Interpretation: Patients with lineage B.1.1.7 were at increased risk of CCU admission and 28-day mortality compared with patients with non-B.1.1.7 SARS-CoV-2. For patients receiving critical care, mortality appeared to be independent of virus strain. Our findings emphasise the importance of measures to control exposure to and infection with COVID-19., Funding: Wellcome Trust, National Institute for Health Research Oxford Biomedical Research Centre, and the Medical Sciences Division of the University of Oxford., Competing Interests: Competing interests JH-C reports receiving grants from the Wellcome trust, Health Data Research-UK, National Institute for Health Research (NIHR) Biomedical Research Centre (Oxford), John Fell Oxford University Press Research Fund, Cancer Research UK, and Oxford Wellcome Institutional Strategic Support Fund; being a member of SAGE subgroups on ethnicity and chair of the NERVTAG risk stratification subgroup; being an unpaid director of QResearch; and being a founder and former director of ClinRisk, outside the submitted work. PST reports previous consultations with AstraZeneca and Duke-National University of Singapore, outside the submitted work. PW was Chief Medical Officer for Sensyne Health, his department received research funding from Sensyne Health, and he holds shares in the company; and he received grant funding from the National Institute for Health Research and Wellcome Trust., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: Pancreatic cancer has the worst survival rate among all cancers. Almost 70% of patients in the UK were diagnosed at Stage IV., Aim: This study aimed to investigate the symptoms associated with the diagnoses of pancreatic ductal adenocarcinoma (PDAC) and pancreatic neuroendocrine neoplasms (PNEN), and comparatively characterise the symptomatology between the two tumour types to inform earlier diagnosis., Design and Setting: A nested case-control study in primary care was conducted using data from the QResearch ® database. Patients aged ≥25 years and diagnosed with PDAC or PNEN during 2000 to 2019 were included as cases. Up to 10 controls from the same general practice were matched with each case by age, sex, and calendar year using incidence density sampling., Method: Conditional logistic regression was used to investigate the association between the 42 shortlisted symptoms and the diagnoses of PDAC and (or) PNEN in different timeframes relative to the index date, adjusting for patients' sociodemographic characteristics, lifestyle, and relevant comorbidities., Results: A total of 23 640 patients were identified as diagnosed with PDAC and 596 with PNEN. Of the symptoms identified, 23 were significantly associated with PDAC, and nine symptoms with PNEN. The two alarm symptoms for both tumours were jaundice and gastrointestinal bleeding. The two newly identified symptoms for PDAC were thirst and dark urine. The risk of unintentional weight loss may be longer than 2 years before the diagnosis of PNEN., Conclusion: PDAC and PNEN have overlapping symptom profiles. The QCancer ® (pancreas) risk prediction model could be updated by including the newly identified symptoms and comorbidities, which could help GPs identify high-risk patients for timely investigation in primary care., (© The Authors.)

Objective: To assess the risks of developing dementia associated with different types and durations of menopausal hormone therapy., Design: Two nested case-control studies., Setting: UK general practices contributing to QResearch or the Clinical Practice Research Datalink (CPRD), using all links to hospital, mortality, and social deprivation data., Participants: 118 501 women aged 55 and older with a primary diagnosis of dementia between 1998 and 2020, matched by age, general practice, and index date to 497 416 female controls., Main Outcome Measures: Dementia diagnoses from general practice, mortality, and hospital records; odds ratios for menopausal hormone treatments adjusted for demographics, smoking status, alcohol consumption, comorbidities, family history, and other prescribed drugs., Results: Overall, 16 291 (14%) women with a diagnosis of dementia and 68 726 (14%) controls had used menopausal hormone therapy more than three years before the index date. Overall, no increased risks of developing dementia associated with menopausal hormone therapy were observed. A decreased global risk of dementia was found among cases and controls younger than 80 years who had been taking oestrogen-only therapy for 10 years or more (adjusted odds ratio 0.85, 95% confidence interval 0.76 to 0.94). Increased risks of developing specifically Alzheimer's disease were found among women who had used oestrogen-progestogen therapy for between five and nine years (1.11, 1.04 to 1.20) and for 10 years or more (1.19, 1.06 to 1.33). This was equivalent to, respectively, five and seven extra cases per 10 000 woman years. Detailed risk associations for the specific progestogens studied are also provided., Conclusion: This study gives estimates for risks of developing dementia and Alzheimer's disease in women exposed to different types of menopausal hormone therapy for different durations and has shown no increased risks of developing dementia overall. It has shown a slightly increased risk of developing Alzheimer's disease among long term users of oestrogen-progestogen therapies., Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from the NIHR Schools for Primary Care Research at Nottingham and Oxford Universities; JHC has received grants from the NIHR Biomedical Research Centre, Oxford, John Fell Oxford University Press Research Fund, Cancer Research UK (grant number C5255/A18085) through the Cancer Research UK Oxford Centre, and the Oxford Wellcome Institutional Strategic Support Fund (204826/Z/16/Z) during the conduct of the study; JHC is an unpaid director of QResearch, a not-for-profit organisation that is a partnership between the University of Oxford and EMIS Health, which supplies the QResearch database used for this work; JHC is a founder and shareholder of ClinRisk Ltd and was its medical director until 31 May 2019; ClinRisk Ltd produces open and closed source software to implement clinical risk algorithms (outside this work) into clinical computer systems; no other relationships or activities that could appear to have influenced the submitted work., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: To derive and validate risk prediction algorithms to estimate the risk of covid-19 related mortality and hospital admission in UK adults after one or two doses of covid-19 vaccination., Design: Prospective, population based cohort study using the QResearch database linked to data on covid-19 vaccination, SARS-CoV-2 results, hospital admissions, systemic anticancer treatment, radiotherapy, and the national death and cancer registries., Settings: Adults aged 19-100 years with one or two doses of covid-19 vaccination between 8 December 2020 and 15 June 2021., Main Outcome Measures: Primary outcome was covid-19 related death. Secondary outcome was covid-19 related hospital admission. Outcomes were assessed from 14 days after each vaccination dose. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance was evaluated in a separate validation cohort of general practices., Results: Of 6 952 440 vaccinated patients in the derivation cohort, 5 150 310 (74.1%) had two vaccine doses. Of 2031 covid-19 deaths and 1929 covid-19 hospital admissions, 81 deaths (4.0%) and 71 admissions (3.7%) occurred 14 days or more after the second vaccine dose. The risk algorithms included age, sex, ethnic origin, deprivation, body mass index, a range of comorbidities, and SARS-CoV-2 infection rate. Incidence of covid-19 mortality increased with age and deprivation, male sex, and Indian and Pakistani ethnic origin. Cause specific hazard ratios were highest for patients with Down's syndrome (12.7-fold increase), kidney transplantation (8.1-fold), sickle cell disease (7.7-fold), care home residency (4.1-fold), chemotherapy (4.3-fold), HIV/AIDS (3.3-fold), liver cirrhosis (3.0-fold), neurological conditions (2.6-fold), recent bone marrow transplantation or a solid organ transplantation ever (2.5-fold), dementia (2.2-fold), and Parkinson's disease (2.2-fold). Other conditions with increased risk (ranging from 1.2-fold to 2.0-fold increases) included chronic kidney disease, blood cancer, epilepsy, chronic obstructive pulmonary disease, coronary heart disease, stroke, atrial fibrillation, heart failure, thromboembolism, peripheral vascular disease, and type 2 diabetes. A similar pattern of associations was seen for covid-19 related hospital admissions. No evidence indicated that associations differed after the second dose, although absolute risks were reduced. The risk algorithm explained 74.1% (95% confidence interval 71.1% to 77.0%) of the variation in time to covid-19 death in the validation cohort. Discrimination was high, with a D statistic of 3.46 (95% confidence interval 3.19 to 3.73) and C statistic of 92.5. Performance was similar after each vaccine dose. In the top 5% of patients with the highest predicted covid-19 mortality risk, sensitivity for identifying covid-19 deaths within 70 days was 78.7%., Conclusion: This population based risk algorithm performed well showing high levels of discrimination for identifying those patients at highest risk of covid-19 related death and hospital admission after vaccination., Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from the NIHR for the submitted work; JH-C reports grants from NIHR Biomedical Research Centre, Oxford, grants from John Fell Oxford University Press Research Fund, grants from Cancer Research UK, through the Cancer Research UK Oxford Centre, grants from the Oxford Wellcome Institutional Strategic Support Fund and other research councils, during the conduct of the study; JH-C is an unpaid director of QResearch, a not-for-profit organisation that is a partnership between the University of Oxford and EMIS Health, which supplied the QResearch database used for this work; JH-C is a founder and shareholder of ClinRisk and was its medical director until 31 May 2019 (ClinRisk produces open and closed source software to implement clinical risk algorithms (outside this work) into clinical computer systems); JH-C is chair of the NERVTAG risk stratification subgroup and a member of SAGE covid-19 groups and the NHS group advising on prioritisation of use of monoclonal antibodies in covid-19 infection; CACC reports receiving personal fees from ClinRisk outside this work, and is a member of the NERVTAG risk stratification subgroup; KK is supported by the NIHR Applied Research Collaboration-East Midlands and the Leicester BRC and is a member of SAGE; RHK was supported by a UKRI Future Leaders Fellowship (MR/S017968/1); KD-O was supported by a grant from the Alan Turing Institute Health Programme (EP/T001569/1); AS is a member of the Scottish Government Chief Medical Officer’s covid-19 advisory group and a member of AstraZeneca’s thrombotic thrombocytopenic advisory group (both roles are unremunerated); RAL is a member of the Welsh government covid-19 technical advisory group (unrenumerated); MGS reports grants from Department of Health and Social Care NIHR UK, grants from the UK Medical Research Council, grants from Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool, during the conduct of the study, and other funds from Integrum Scientific, Greensboro, NC, USA, outside the submitted work; MGS is a member of NERVTAG and attends SAGE covid-19; AH is a member of NERVTAG and the NHS group advising on prioritisation of use of monoclonal antibodies in covid-19 infection; JV is national clinical director for diabetes and obesity at NHS England and Improvement; FK is a member of the Northern Ireland Chief Medical Officer’s pandemic modelling group and strategic intelligence group; JSN-V-T is seconded to the Department of Health and Social Care, England. The views expressed in this manuscript are those of the authors and not necessarily those of Department of Health and Social Care or the UK government., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. No commercial re-use. See rights and permissions. Published by BMJ.)

Importance: Although children mainly experience mild COVID-19 disease, hospitalization rates are increasing, with limited understanding of underlying factors. There is an established association between race and severe COVID-19 outcomes in adults in England; however, whether a similar association exists in children is unclear., Objective: To investigate the association between race and childhood COVID-19 testing and hospital outcomes., Design, Setting, Participants: In this cohort study, children (0-18 years of age) from participating family practices in England were identified in the QResearch database between January 24 and November 30, 2020. The QResearch database has individually linked patients with national SARS-CoV-2 testing, hospital admission, and mortality data., Exposures: The main characteristic of interest is self-reported race. Other exposures were age, sex, deprivation level, geographic region, household size, and comorbidities (asthma; diabetes; and cardiac, neurologic, and hematologic conditions)., Main Outcomes and Measures: The primary outcome was hospital admission with confirmed COVID-19. Secondary outcomes were SARS-CoV-2-positive test result and any hospital attendance with confirmed COVID-19 and intensive care admission., Results: Of 2 576 353 children (mean [SD] age, 9.23 [5.24] years; 48.8% female), 410 726 (15.9%) were tested for SARS-CoV-2 and 26 322 (6.4%) tested positive. A total of 1853 children (0.07%) with confirmed COVID-19 attended hospital, 343 (0.01%) were admitted to the hospital, and 73 (0.002%) required intensive care. Testing varied across race. White children had the highest proportion of SARS-CoV-2 tests (223 701/1 311 041 [17.1%]), whereas Asian children (33 213/243 545 [13.6%]), Black children (7727/93 620 [8.3%]), and children of mixed or other races (18 971/147 529 [12.9%]) had lower proportions. Compared with White children, Asian children were more likely to have COVID-19 hospital admissions (adjusted odds ratio [OR], 1.62; 95% CI, 1.12-2.36), whereas Black children (adjusted OR, 1.44; 95% CI, 0.90-2.31) and children of mixed or other races (adjusted OR, 1.40; 95% CI, 0.93-2.10) had comparable hospital admissions. Asian children were more likely to be admitted to intensive care (adjusted OR, 2.11; 95% CI, 1.07-4.14), and Black children (adjusted OR, 2.31; 95% CI, 1.08-4.94) and children of mixed or other races (adjusted OR, 2.14; 95% CI, 1.25-3.65) had longer hospital admissions (≥36 hours)., Conclusions and Relevance: In this large population-based study exploring the association between race and childhood COVID-19 testing and hospital outcomes, several race-specific disparities were observed in severe COVID-19 outcomes. However, ascertainment bias and residual confounding in this cohort study should be considered before drawing any further conclusions. Overall, findings of this study have important public health implications internationally.

Background: The NHS Health Check cardiovascular prevention programme is now 10 years old., Aim: To describe NHS Heath Check attendance, new diagnoses, and treatment in relation to equity indicators., Design and Setting: A nationally representative database derived from 1500 general practices from 2009-2017., Method: The authors compared NHS Health Check attendance and new diagnoses and treatments by age, sex, ethnic group, and deprivation., Results: In 2013-2017, 590 218 (16.9%) eligible people aged 40-74 years attended an NHS Health Check and 2 902 598 (83.1%) did not attend. South Asian ethnic groups were most likely to attend compared to others, and females more than males. New diagnoses were more likely in attendees than non-attendees: hypertension 25/1000 in attendees versus 9/1000 in non-attendees; type 2 diabetes 8/1000 versus 3/1000; and chronic kidney disease (CKD) 7/1000 versus 4/1000. In people aged ≥65 years, atrial fibrillation was newly diagnosed in 5/1000 attendees and 3/1000 non-attendees, and for dementia 2/1000 versus 1/1000, respectively. Type 2 diabetes, hypertension, and CKD were more likely in more deprived groups, and in South Asian, Black African, and Black Caribbean ethnic groups. Attendees were more likely to be prescribed statins (26/1000) than non-attendees (8/1000), and antihypertensive medicines (25/1000 versus 13/1000 non-attendees). However, of the 117 963 people with ≥10% CVD risk who were eligible for statins, only 9785 (8.3%) were prescribed them., Conclusion: Uptake of NHS Health Checks remains low. Attendees were more likely than non-attendees to be diagnosed with type 2 diabetes, hypertension, and CKD, and to receive treatment with statins and antihypertensives. Most attendees received neither treatment nor referral. Of those eligible for statins, <10% were treated. Policy reviews should consider a targeted approach prioritising those at highest CVD risk for face-to-face contact and consider other options for those at lower CVD risk., (© The Authors.)

Introduction: Hormone replacement therapy (HRT) can help women experiencing menopausal symptoms, but usage has declined due to uncertainty around risks of cancer and some cardiovascular diseases (CVD). Moreover, improved cancer survival rates mean that more women who survive cancer go on to experience menopausal symptoms. Understanding these relationships is important so that women and their clinicians can make informed decisions around the risks and benefits of HRT. This study's primary aim is to determine the association between HRT use after cancer diagnosis and the risk of cancer-specific mortality. The secondary aims are to investigate the risks of HRT on subsequent cancer, all-cause mortality and CVD., Methods and Analysis: We will conduct a population-based longitudinal cohort study of 18-79 year-old women diagnosed with cancer between 1998 and 2020, using the QResearch database. The main exposure is HRT use, categorised based on compound, dose and route of administration, and modelled as a time-varying covariate. Analysis of HRT use precancer and postcancer diagnosis will be conducted separately. The primary outcome is cancer-specific mortality, which will be stratified by cancer site. Secondary outcomes include subsequent cancer diagnosis, CVD (including venous thrombo-embolism) and all-cause mortality. Adjustment will be made for key confounders such as age, body mass index, ethnicity, deprivation index, comorbidities, and cancer grade, stage and treatment. Statistical analysis will include descriptive statistics and Cox proportional hazards models to calculate HRs and 95% CIs., Ethics and Dissemination: Ethical approval for this project was obtained from the QResearch Scientific Committee (Ref: OX24, project title 'Use of hormone replacement therapy and survival from cancer'). This project has been, and will continue to be, supported by patient and public involvement panels. We intend to the submit the findings for peer-reviewed publication in an academic journal and disseminate them to the public through Cancer Research UK., Competing Interests: Competing interests: JH-C is an unpaid director of the Qresearch database (partnership between the University of Oxford and EMIS, commercial supplier of GP computer systems). She is shareholder and former director of ClinRisk Ltd outside the submitted work. PST reports consulting with AstraZeneca and Duke-NUS outside the submitted work., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

Background: Previous studies suggested that the prevalence of chronic respiratory disease in patients hospitalised with COVID-19 was lower than its prevalence in the general population. The aim of this study was to assess whether chronic lung disease or use of inhaled corticosteroids (ICS) affects the risk of contracting severe COVID-19., Methods: In this population cohort study, records from 1205 general practices in England that contribute to the QResearch database were linked to Public Health England's database of SARS-CoV-2 testing and English hospital admissions, intensive care unit (ICU) admissions, and deaths for COVID-19. All patients aged 20 years and older who were registered with one of the 1205 general practices on Jan 24, 2020, were included in this study. With Cox regression, we examined the risks of COVID-19-related hospitalisation, admission to ICU, and death in relation to respiratory disease and use of ICS, adjusting for demographic and socioeconomic status and comorbidities associated with severe COVID-19., Findings: Between Jan 24 and April 30, 2020, 8 256 161 people were included in the cohort and observed, of whom 14 479 (0·2%) were admitted to hospital with COVID-19, 1542 (<0·1%) were admitted to ICU, and 5956 (0·1%) died. People with some respiratory diseases were at an increased risk of hospitalisation (chronic obstructive pulmonary disease [COPD] hazard ratio [HR] 1·54 [95% CI 1·45-1·63], asthma 1·18 [1·13-1·24], severe asthma 1·29 [1·22-1·37; people on three or more current asthma medications], bronchiectasis 1·34 [1·20-1·50], sarcoidosis 1·36 [1·10-1·68], extrinsic allergic alveolitis 1·35 [0·82-2·21], idiopathic pulmonary fibrosis 1·59 [1·30-1·95], other interstitial lung disease 1·66 [1·30-2·12], and lung cancer 2·24 [1·89-2·65]) and death (COPD 1·54 [1·42-1·67], asthma 0·99 [0·91-1·07], severe asthma 1·08 [0·98-1·19], bronchiectasis 1·12 [0·94-1·33], sarcoidosis 1·41 [0·99-1·99), extrinsic allergic alveolitis 1·56 [0·78-3·13], idiopathic pulmonary fibrosis 1·47 [1·12-1·92], other interstitial lung disease 2·05 [1·49-2·81], and lung cancer 1·77 [1·37-2·29]) due to COVID-19 compared with those without these diseases. Admission to ICU was rare, but the HR for people with asthma was 1·08 (0·93-1·25) and severe asthma was 1·30 (1·08-1·58). In a post-hoc analysis, relative risks of severe COVID-19 in people with respiratory disease were similar before and after shielding was introduced on March 23, 2020. In another post-hoc analysis, people with two or more prescriptions for ICS in the 150 days before study start were at a slightly higher risk of severe COVID-19 compared with all other individuals (ie, no or one ICS prescription): HR 1·13 (1·03-1·23) for hospitalisation, 1·63 (1·18-2·24) for ICU admission, and 1·15 (1·01-1·31) for death., Interpretation: The risk of severe COVID-19 in people with asthma is relatively small. People with COPD and interstitial lung disease appear to have a modestly increased risk of severe disease, but their risk of death from COVID-19 at the height of the epidemic was mostly far lower than the ordinary risk of death from any cause. Use of inhaled steroids might be associated with a modestly increased risk of severe COVID-19., Funding: National Institute for Health Research Oxford Biomedical Research Centre and the Wellcome Trust., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: Public policy measures and clinical risk assessments relevant to COVID-19 need to be aided by risk prediction models that are rigorously developed and validated. We aimed to externally validate a risk prediction algorithm (QCovid) to estimate mortality outcomes from COVID-19 in adults in England., Methods: We did a population-based cohort study using the UK Office for National Statistics Public Health Linked Data Asset, a cohort of individuals aged 19-100 years, based on the 2011 census and linked to Hospital Episode Statistics, the General Practice Extraction Service data for pandemic planning and research, and radiotherapy and systemic chemotherapy records. The primary outcome was time to COVID-19 death, defined as confirmed or suspected COVID-19 death as per death certification. Two periods were used: (1) Jan 24 to April 30, 2020, and (2) May 1 to July 28, 2020. We assessed the performance of the QCovid algorithms using measures of discrimination and calibration. Using predicted 90-day risk of COVID-19 death, we calculated r 2 values, Brier scores, and measures of discrimination and calibration with corresponding 95% CIs over the two time periods., Findings: We included 34 897 648 adults aged 19-100 years resident in England. 26 985 (0·08%) COVID-19 deaths occurred during the first period and 13 177 (0·04%) during the second. The algorithms had good discrimination and calibration in both periods. In the first period, they explained 77·1% (95% CI 76·9-77·4) of the variation in time to death in men and 76·3% (76·0-76·6) in women. The D statistic was 3·761 (3·732-3·789) for men and 3·671 (3·640-3·702) for women and Harrell's C was 0·935 (0·933-0·937) for men and 0·945 (0·943-0·947) for women. Similar results were obtained for the second time period. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths in the first period was 65·94% for men and 71·67% for women., Interpretation: The QCovid population-based risk algorithm performed well, showing high levels of discrimination for COVID-19 deaths in men and women for both time periods. QCovid has the potential to be dynamically updated as the pandemic evolves and, therefore, has potential use in guiding national policy., Funding: UK National Institute for Health Research., Competing Interests: Declaration of interests JH-C reports grants from the National Institute for Health Research Biomedical Research Centre, Oxford, UK; John Fell Oxford University Press Research Fund; Cancer Research UK, through the Cancer Research UK Oxford Centre; and the Oxford Wellcome Institutional Strategic Support Fund, during the conduct of the study. JH-C is an unpaid director of QResearch, a not-for-profit organisation that is a partnership between the University of Oxford, Oxford, UK, and EMIS Health, who supplied the QResearch database used for this work. JH-C is a founder and shareholder of ClinRisk and was the company's medical director until May 31, 2019. ClinRisk produces open and closed source software to implement clinical risk algorithms (outside this work) into clinical computer systems. All other authors declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: Obesity is a major risk factor for adverse outcomes after infection with SARS-CoV-2. We aimed to examine this association, including interactions with demographic and behavioural characteristics, type 2 diabetes, and other health conditions., Methods: In this prospective, community-based, cohort study, we used de-identified patient-level data from the QResearch database of general practices in England, UK. We extracted data for patients aged 20 years and older who were registered at a practice eligible for inclusion in the QResearch database between Jan 24, 2020 (date of the first recorded infection in the UK) and April 30, 2020, and with available data on BMI. Data extracted included demographic, clinical, clinical values linked with Public Health England's database of positive SARS-CoV-2 test results, and death certificates from the Office of National Statistics. Outcomes, as a proxy measure of severe COVID-19, were admission to hospital, admission to an intensive care unit (ICU), and death due to COVID-19. We used Cox proportional hazard models to estimate the risk of severe COVID-19, sequentially adjusting for demographic characteristics, behavioural factors, and comorbidities., Findings: Among 6 910 695 eligible individuals (mean BMI 26·78 kg/m 2 [SD 5·59]), 13 503 (0·20%) were admitted to hospital, 1601 (0·02%) to an ICU, and 5479 (0·08%) died after a positive test for SARS-CoV-2. We found J-shaped associations between BMI and admission to hospital due to COVID-19 (adjusted hazard ratio [HR] per kg/m 2 from the nadir at BMI of 23 kg/m 2 of 1·05 [95% CI 1·05-1·05]) and death (1·04 [1·04-1·05]), and a linear association across the whole BMI range with ICU admission (1·10 [1·09-1·10]). We found a significant interaction between BMI and age and ethnicity, with higher HR per kg/m 2 above BMI 23 kg/m 2 for younger people (adjusted HR per kg/m 2 above BMI 23 kg/m 2 for hospital admission 1·09 [95% CI 1·08-1·10] in 20-39 years age group vs 80-100 years group 1·01 [1·00-1·02]) and Black people than White people (1·07 [1·06-1·08] vs 1·04 [1·04-1·05]). The risk of admission to hospital and ICU due to COVID-19 associated with unit increase in BMI was slightly lower in people with type 2 diabetes, hypertension, and cardiovascular disease than in those without these morbidities., Interpretation: At a BMI of more than 23 kg/m 2 , we found a linear increase in risk of severe COVID-19 leading to admission to hospital and death, and a linear increase in admission to an ICU across the whole BMI range, which is not attributable to excess risks of related diseases. The relative risk due to increasing BMI is particularly notable people younger than 40 years and of Black ethnicity., Funding: NIHR Oxford Biomedical Research Centre., Competing Interests: Declaration of interests NMA, PA, and SAJ were investigators on a trial of total diet replacement for weight loss funded by a grant from Cambridge Weight Plan UK to the University of Oxford (Oxford, UK) and received no personal payments from this. PA spoke at a symposium at the Royal College of General Practitioners annual conference on interventions for weight loss that was funded by Novo Nordisk and received no personal payments. JH-C reports grants from National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), John Fell Oxford University Press Research Fund, Cancer Research UK (C5255/A18085) through the Cancer Research UK Oxford Centre, and Oxford Wellcome Institutional Strategic Support Fund (204826/Z/16/Z) during the conduct of the study. JH-C received personal fees and other support from ClinRisk (until 2019) outside of the submitted work, and is an unpaid director of QResearch, a not-for-profit organisation that is a partnership between the University of Oxford and Egton Medical Information Systems (EMIS) Health who supply the QResearch database used for this work. All other authors declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: Diagnosis of prostate cancer at an early stage can potentially identify tumours when intervention may improve treatment options and survival., Aim: To develop and validate an equation to predict absolute risk of prostate cancer in asymptomatic men with prostate specific antigen (PSA) tests in primary care., Design and Setting: Cohort study using data from English general practices, held in the QResearch database., Method: Routine data were collected from 1098 QResearch English general practices linked to mortality, hospital, and cancer records for model development. Two separate sets of practices were used for validation. In total, there were 844 455 men aged 25-84 years with PSA tests recorded who were free of prostate cancer at baseline in the derivation cohort; the validation cohorts comprised 292 084 and 316 583 men. The primary outcome was incident prostate cancer. Cox proportional hazards models were used to derive 10-year risk equations. Measures of performance were determined in both validation cohorts., Results: There were 40 821 incident cases of prostate cancer in the derivation cohort. The risk equation included PSA level, age, deprivation, ethnicity, smoking status, serious mental illness, diabetes, BMI, and family history of prostate cancer. The risk equation explained 70.4% (95% CI = 69.2 to 71.6) of the variation in time to diagnosis of prostate cancer ( R 2 ) (D statistic 3.15, 95% CI = 3.06 to 3.25; Harrell's C-index 0.917, 95% CI = 0.915 to 0.919). Two-step approach had higher sensitivity than a fixed PSA threshold at identifying prostate cancer cases (identifying 68.2% versus 43.9% of cases), high-grade cancers (49.2% versus 40.3%), and deaths (67.0% versus 31.5%)., Conclusion: The risk equation provided valid measures of absolute risk and had higher sensitivity for incident prostate cancer, high-grade cancers, and prostate cancer mortality than a simple approach based on age and PSA threshold., (© The Authors.)

Introduction: Recent evidence suggests that ethnic minority groups are disproportionately at increased risk of hospitalisation and death from SARS-CoV-2 infection. Population-based evidence on potential explanatory factors across minority groups and within subgroups is lacking. This study aims to quantify the association between ethnicity and the risk of hospitalisation and mortality due to COVID-19., Methods and Analysis: This is a retrospective cohort study of adults registered across a representative and anonymised national primary care database (QResearch) that includes data on 10 million people in England. Sociodemographic, deprivation, clinical and domicile characteristics will be summarised and compared across ethnic subgroups (categorised as per 2011 census). Cox models will be used to calculate HR for hospitalisation and COVID-19 mortality associated with ethnic group. Potential confounding and explanatory factors (such as demographic, socioeconomic and clinical) will be adjusted for within regression models. The percentage contribution of distinct risk factor classes to the excess risks seen in ethnic groups/subgroups will be calculated., Ethics and Dissemination: The study has undergone ethics review in accordance with the QResearch agreement (reference OX102). Findings will be disseminated through peer-reviewed manuscripts, presentations at scientific meetings and conferences with national and international stakeholders., Competing Interests: Competing interests: JH-C is founder and director of QResearch database, which is a not-for-profit organisation with EMIS (leading commercial supplier of IT for 55% of general practices in the UK). JH-C is co-owner of ClinRisk and was a paid director until June 2019. ClinRisk develops open and closed source software to ensure the reliable and updatable implementation of clinical risk equations within clinical computer systems to help improve patient care, outside the submitted work. KK is national lead for NIHR ARC ethnicity and diversity, Director for the University of Leicester Centre for BME Health, Trustee of the South Asian Health Foundation and member of the Independent SAGE. The authors declare that no support from any organisation and no financial relationships have influenced the submitted work., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

Background: There is considerable interest in whether genetic data can be used to improve standard cardiovascular disease risk calculators, as the latter are routinely used in clinical practice to manage preventative treatment., Methods: Using the UK Biobank resource, we developed our own polygenic risk score for coronary artery disease (CAD). We used an additional 60 000 UK Biobank individuals to develop an integrated risk tool (IRT) that combined our polygenic risk score with established risk tools (either the American Heart Association/American College of Cardiology pooled cohort equations [PCE] or UK QRISK3), and we tested our IRT in an additional, independent set of 186 451 UK Biobank individuals., Results: The novel CAD polygenic risk score shows superior predictive power for CAD events, compared with other published polygenic risk scores, and is largely uncorrelated with PCE and QRISK3. When combined with PCE into an IRT, it has superior predictive accuracy. Overall, 10.4% of incident CAD cases were misclassified as low risk by PCE and correctly classified as high risk by the IRT, compared with 4.4% misclassified by the IRT and correctly classified by PCE. The overall net reclassification improvement for the IRT was 5.9% (95% CI, 4.7-7.0). When individuals were stratified into age-by-sex subgroups, the improvement was larger for all subgroups (range, 8.3%-15.4%), with the best performance in 40- to 54-year-old men (15.4% [95% CI, 11.6-19.3]). Comparable results were found using a different risk tool (QRISK3) and also a broader definition of cardiovascular disease. Use of the IRT is estimated to avoid up to 12 000 deaths in the United States over a 5-year period., Conclusions: An IRT that includes polygenic risk outperforms current risk stratification tools and offers greater opportunity for early interventions. Given the plummeting costs of genetic tests, future iterations of CAD risk tools would be enhanced with the addition of a person's polygenic risk.

Background: Hormone replacement therapy (HRT) is widely used and has proven benefits for women with menopausal symptoms. An increasing number of women with cancer experience menopausal symptoms but the safety of HRT use in women with cancer is unclear. There are particular concerns that HRT could accelerate cancer progression in women with cancer, and also that HRT could increase the risk of cardiovascular disease in such women. Therefore, our primary aim is to determine whether HRT use alters the risk of cancer-specific mortality in women with a range of common cancers. Our secondary objectives are to investigate whether HRT alters the risk of second cancers, cardiovascular disease, venous thromboembolism and all-cause mortality., Methods: The study will utilise independent population-based data from Wales using the SAIL databank and Scotland based upon the national Prescribing Information System. The study will include women newly diagnosed with common cancers from 2000 to 2016, identified from cancer registries. Women with breast cancers will be excluded. HRT will be ascertained using electronic prescribing in Wales or dispensing records in Scotland. The primary outcome will be time to cancer-specific mortality from national mortality records. Time-dependent cox regression models will be used to calculate hazard ratios (HR) and 95% confidence intervals (95% CIs) for cancer specific death in HRT users compared with non-users after cancer diagnosis after adjusting for relevant confounders, stratified by cancer site. Analysis will be repeated investigating the impact of HRT use immediately before cancer diagnosis. Secondary analyses will be conducted on the risk of second cancers, cardiovascular disease, venous thromboembolism and all-cause mortality. Analyses will be conducted within each cohort and pooled across cohorts., Discussion: Our study will provide evidence to inform guidance given to women diagnosed with cancer on the safety of HRT use and/or guide modifications to clinical practice.

The use of big data containing millions of primary care medical records provides an opportunity for rapid research to help inform patient care and policy decisions during the first and subsequent waves of the coronavirus disease 2019 (COVID-19) pandemic. Routinely collected primary care data have previously been used for national pandemic surveillance, quantifying associations between exposures and outcomes, identifying high risk populations, and examining the effects of interventions at scale, but there is no consensus on how to effectively conduct or report these data for COVID-19 research. A COVID-19 primary care database consortium was established in April 2020 and its researchers have ongoing COVID-19 projects in overlapping data sets with over 40 million primary care records in the United Kingdom that are variously linked to public health, secondary care, and vital status records. This consensus agreement is aimed at facilitating transparency and rigor in methodological approaches, and consistency in defining and reporting cases, exposures, confounders, stratification variables, and outcomes in relation to the pharmacoepidemiology of COVID-19. This will facilitate comparison, validation, and meta-analyses of research during and after the pandemic., (© 2021 Annals of Family Medicine, Inc.)

Background: Prostate cancer is a leading cause of cancer- related death. Interpreting the results from trials of screening with prostate-specific antigen (PSA) is complex in terms of defining optimal prostate cancer screening policy., Aim: To assess the rates of, and factors associated with, the uptake of PSA testing and opportunistic screening (that is, a PSA test in the absence of any symptoms) in England between 1998 and 2017, and to estimate the likely rates of pre-randomisation screening and contamination (that is, unscheduled screening in the 'control' arm) of the UK-based Cluster Randomised Trial of PSA Testing for Prostate Cancer (CAP)., Design and Setting: Open cohort study of men in England aged 40-75 years at cohort entry (1998-2017), undertaken using the QResearch database., Method: Eligible men were followed for up to 19 years. Rates of PSA testing and opportunistic PSA screening were calculated; Cox regression was used to estimate associations., Results: The cohort comprised 2 808 477 men, of whom 631 426 had a total of 1 720 855 PSA tests. The authors identified that 410 724 men had opportunistic PSA screening. Cumulative proportions of uptake of opportunistic screening in the cohort were 9.96% at 5 years', 22.71% at 10 years', and 44.13% at 19 years' follow-up. The potential rate of contamination in the CAP control arm was estimated at 24.50%., Conclusion: A substantial number of men in England opt in to opportunistic prostate cancer screening, despite uncertainty regarding its efficacy and harms. The rate of opportunistic prostate cancer screening in the population is likely to have contaminated the CAP trial, making it difficult to interpret the results., (© The Authors.)

Background: A rising incidence and high mortality were found for bullous pemphigoid (BP) over a decade ago in the UK. Updated estimates of its epidemiology are required to understand the healthcare needs of an ageing population., Objectives: To determine the incidence, prevalence and mortality rates of BP in England from 1998 to 2017., Methods: We conducted a cohort study of longitudinal electronic health records using the Clinical Practice Research Datalink and linked Hospital Episode Statistics. Incidence was calculated per 100 000 person-years and annual point prevalence per 100 000 people. Multivariate analysis was used to determine incidence rate ratios by sociodemographic factors. Mortality was examined in an age-, sex- and practice-matched cohort, using linked Office of National Statistics death records. Hazard ratios (HRs) were stratified by matched set., Results: The incidence was 7·63 [95% confidence interval (CI) 7·35-7·93] per 100 000 person-years and rose with increasing age, particularly for elderly men. The annual increase in incidence was 0·9% (95% CI 0·2-1·7). The prevalence almost doubled over the observation period, reaching 47·99 (95% CI 43·09-53·46) per 100 000 people and 141·24 (95% CI 125·55-158·87) per 100 000 people over the age of 60 years. The risk of all-cause mortality was highest in the 2 years after diagnosis (HR 2·96; 95% CI 2·68-3·26) and remained raised thereafter (HR 1·54; 95% CI 1·36-1·74)., Conclusions: We report a modest increase in the incidence rate of BP, but show that the burden of disease in the elderly population is considerable. Mortality is high, particularly in the first 2 years after diagnosis., (© 2020 The Authors. British Journal of Dermatology published by John Wiley & Sons Ltd on behalf of British Association of Dermatologists.)