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Abstract Background The diagnosis of B-cell lymphoma, one of the commonest cancers seen in childhood and adolescence, is challenging. There is a crucial need to identify and delineate the prevalence of associated symptoms in order to improve early diagnosis. Aims To identify clinical presentations associated with childhood and adolescent B-cell lymphomas and estimate symptom prevalence. Methods A systematic review of observational studies and meta-analysis of proportions was carried out. Medline and EMBASE were systematically searched, with no language restrictions, from inception to 1st August 2022. Observational studies with at least 10 participants, exploring clinical presentations of any childhood and adolescent lymphoma, were selected. Proportions from each study were inputted to determine the weighted average (pooled) proportion, through random-effects meta-analysis. Results Studies reported on symptoms, signs and presentation sites at diagnosis of 12,207 children and adolescents up to the age of 20. Hodgkin’s lymphoma most frequently presented with adenopathy in the head-and-neck region (79% [95% CI 58%-91%]), whilst non-Hodgkin’s lymphoma presented abdominally (55% [95% CI 43%-68%]). Symptoms associated with lymphoma included cervical lymphadenopathy (48% [95% CI 20%-77%]), peripheral lymphadenopathy (51% [95% CI 37%-66%]), B-symptoms (40% [95% CI 34%-44%]), fever (43% [95% CI 34%-54%]), abdominal mass (46% [95% CI 29%-64%]), weight loss (53% [95% CI 39%-66%]), head-and-neck mass (21% [95% CI 6%-47%]), organomegaly (29% [95% CI 23%-37%]), night sweats (19% [95% CI 10%-32%]), abdominal pain (28% [95% CI 15%-47%]), bone pain (17% [95% CI 10%-28%]) and abnormal neurology (11% [95% CI 3%-28%]). Conclusion This systematic review and meta-analysis of proportions provides insight into the heterogeneous clinical presentations of B-cell lymphoma in childhood and adolescence and provides estimates of symptom prevalence. This information is likely to increase public and clinical awareness of lymphoma presentations and aid earlier diagnosis. This review further highlights the lack of studies exploring childhood and adolescent lymphoma presentations in primary care, where patients are likely to present at the earliest stages of their disease.

Abstract Background Trials of novel agents are required to improve the care of patients with rare diseases, but trial feasibility may be uncertain due to concerns over insufficient patient numbers. We aimed to determine the size of the pool of potential participants in England 2015–2017 for trials in the autoimmune blistering skin disease bullous pemphigoid. Methods The size of the pool of potential participants was estimated using routinely collected healthcare data from linked primary care (Clinical Practice Research Datalink; CPRD) and secondary care (Hospital Episode Statistics; HES) databases. Thirteen consultant dermatologists were surveyed to determine the likelihood that a patient would be eligible for a trial based on the presence of cautions or contra-indications to prednisolone use. These criteria were applied to determine how they influenced the potential pool of participants. Results Extrapolated to the population of England, we would expect approximately 10,800 (point estimate 10,747; 95% CI 7191 to 17,239) new cases of bullous pemphigoid to be identified in a three-year period. For a future trial involving oral prednisolone (standard care), the application of cautions to its use as exclusion criteria would result in approximately 365 potential participants unlikely to be recruited, a further 5332 could be recruited with caution, and 5104 in whom recruitment is still possible. 11–17% of potential participants may have pre-existing dementia and require an alternative consent process. Conclusions Routinely collected electronic health records can be used to inform the feasibility of clinical trials in rare diseases, such as whether recruitment is feasible nationally and how long recruitment might take to meet recruitment targets. Future trials of bullous pemphigoid in England may use the data presented to inform trial design, including eligibility criteria and consent processes for enrolling people with dementia.

Objectives: The aim of this study was to establish the relative safety and balance of risks for antidepressant treatment in older people. The study objectives were to (1) determine relative and absolute risks of predefined adverse events in older people with depression, comparing classes of antidepressant drugs [tricyclic and related antidepressants (TCAs), selective serotonin reuptake inhibitors (SSRIs), monoamine oxidase inhibitors (MAOIs) and other antidepressants] and commonly prescribed individual drugs with non-use of antidepressant drugs; (2) directly compare the risk of adverse events for SSRIs with TCAs; (3) determine associations with dose and duration of antidepressant medication; (4) describe patterns of antidepressant use in older people with depression; and (5) estimate costs of antidepressant medication and primary care visits. Design: A cohort study of patients aged 65 years and over diagnosed with depression. Setting: The study was based in 570 general practices in the UK supplying data to the QResearch database. Participants: Patients diagnosed with a new episode of depression between the ages of 65 and 100 years, from 1 January 1996 to 31 December 2007. Participants were followed up until 31 December 2008. Interventions: The exposure of interest was treatment with antidepressant medication. Antidepressant drugs were grouped into the major classes and commonly prescribed individual drugs were identified. Main outcome measures: There were 13 predefined outcome measures: all-cause mortality, sudden cardiac death, suicide, attempted suicide/self-harm, myocardial infarction, stroke/transient ischaemic attack (TIA), falls, fractures, upper gastrointestinal bleeding, epilepsy/seizures, road traffic accidents, adverse drug reactions and hyponatraemia. Results: In total, 60,746 patients were included in the study cohort. Of these, 54,038 (89.0%) received at least one prescription for an antidepressant during follow-up. The associations with the adverse outcomes were significantly different between the classes of antidepressant drugs for seven outcomes. SSRIs were associated with the highest adjusted hazard ratios (HRs) for falls [1.66, 95% confidence interval (CI) 1.58 to 1.73] and hyponatraemia (1.52, 95% CI 1.33 to 1.75), and the group of other antidepressants was associated with the highest HRs for all-cause mortality (1.66, 95% CI 1.56 to 1.77), attempted suicide/self-harm (5.16, 95% CI 3.90 to 6.83), stroke/TIA (1.37, 95% CI 1.22 to 1.55), fracture (1.63, 95% CI 1.45 to 1.83) and epilepsy/seizures (2.24, 95% CI 1.60 to 3.15) compared with when antidepressants were not being used. TCAs did not have the highest HR for any of the outcomes. There were also significantly different associations between the individual drugs for seven outcomes, with trazodone, mirtazapine and venlafaxine associated with the highest rates for several of these outcomes. The mean incremental cost (for all antidepressant prescriptions) ranged between £51.58 (amitriptyline) and £641.18 (venlafaxine) over the 5-year post-diagnosis period. Conclusions: This study found associations between use of antidepressant drugs and a number of adverse events in older people. There was no evidence that SSRIs or drugs in the group of other antidepressants were associated with a reduced risk of any of the adverse outcomes compared with TCAs; however, they may be associated with an increased risk for certain outcomes. Among individual drugs trazodone, mirtazapine and venlafaxine were associated with the highest rates for some outcomes. Indication bias and residual confounding may explain some of the study findings. The risks of prescribing antidepressants need to be weighed against the potential benefits of these drugs. Funding: The National Institute for Health Research Health Technology Assessment programme.

Background: Sotrovimab is a neutralising monoclonal antibody (nMAB) currently available to treat extremely clinically vulnerable COVID-19 patients in England. Trials have shown it to have mild to moderate side effects, however, evidence regarding its safety in real-world settings remains insufficient., Methods: Descriptive and multivariable logistic regression analyses were conducted to evaluate uptake, and a self-controlled case series analysis performed to measure the risk of hospital admission (hospitalisation) associated with 49 pre-specified suspected adverse outcomes in the period 2-28 days post-Sotrovimab treatment among eligible patients treated between December 11, 2021 and May 24, 2022., Results: Here we show that among treated and untreated eligible individuals, the mean ages (54.6 years, SD: 16.1 vs 54.1, SD: 18.3) and sex distribution (women: 60.9% vs 58.1%; men: 38.9% vs 41.1%) are similar. There are marked variations in uptake between ethnic groups, which is higher amongst individuals categorised ethnically as Indian (15.0%; 95%CI 13.8, 16.3), Other Asian (13.7%; 95%CI 11.9, 15.8), white (13.4%; 95%CI 13.3, 13.6), and Bangladeshi (11.4%; 95%CI 8.8, 14.6); and lower amongst Black Caribbean individuals (6.4%; 95%CI 5.4, 7.5) and Black Africans (4.7%; 95%CI 4.1, 5.4). We find no increased risk of any of the suspected adverse outcomes in the period 2-28 days post-treatment., Conclusions: We find no safety signals of concern for possible adverse outcomes in the period 2-28 days post treatment with Sotrovimab. However, there is evidence of unequal uptake of Sotrovimab treatment across ethnic groups., Competing Interests: Competing interests: The authors declare the following competing interests: AS was a member of the Scottish Government Chief Medical Officer’s COVID-19 Advisory Group, the Scottish Government’s Standing Committee on Pandemics, and AstraZeneca’s Thrombotic Thrombocytopenic Advisory Group. All roles were unremunerated. J.H.C. reports grants from the 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. J.H.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. J.H.C. is a founder and previously a shareholder of ClinRisk Ltd. and was its medical director until 31st May 2019 and shareholder until Aug 2023 when 100% of the company was donated to Endeavour Health Care Charitable Trust. ClinRisk Ltd. produces open and closed-source software to implement clinical risk algorithms (outside this work) into clinical computer systems. J.H.C. was chair of the NERVTAG risk stratification subgroup and a member of SAGE COVID-19 groups and the NHS group advising on prioritisation of the use of monoclonal antibodies in SARS-CoV-2 infection. The remaining authors declare no competing interests., (© 2025. The Author(s).)

Background: Antidepressants' effects are established in randomised controlled trials (RCTs), but not in the real world., Aims: To investigate real-world comparative effects of antidepressants for depression and compare them with RCTs., Method: We performed a cohort study based on the QResearch database. We included people with a newly recorded diagnosis of depression, exposed to licensed antidepressants in the UK. We assessed all-cause dropouts (acceptability), dropouts for adverse events (tolerability), occurrence of at least one adverse event (safety), and response and remission on the Patient Health Questionnaire (PHQ)-9 (effectiveness) at 2 and 12 months. Logistic regressions were used to compute adjusted-odds ratio (aOR) with 99% CIs, assessing the associations between exposure to each antidepressant against fluoxetine (comparator) and outcomes of interest. We compared estimates from the real world with RCTs using ratio-of-odds ratio (ROR) with 95% CI., Results: A total of 673 177 depressed people were studied: females 57.1%, mean age 42.8 (s.d. 17.7) years, mean baseline PHQ-9 17.1 (s.d. 5.0) (moderately severe depression). At 2 months, antidepressant acceptability was 61.4%, tolerability 94.4%, safety 54.5%, PHQ-9 decreased to 12.3 (s.d. 6.5). At 12 months, acceptability was 12.3%, tolerability 87.5%, safety 28.8%, PHQ-9 12.9 (s.d. 6.8). In the short and long term, tricyclics, mirtazapine and trazodone were worse than fluoxetine for most outcomes; citalopram had better acceptability than fluoxetine (aOR 0.95; 99% CI 0.92, 0.97), sertraline had lower tolerability (aOR 1.12; 99% CI 1.06, 1.18), and both citalopram and sertraline had lower safety (aOR 1.17 and 1.25, respectively). In the long term, citalopram had better acceptability (aOR 0.78; 99% CI 0.76, 0.81) and effectiveness (aOR 1.12 for both response and remission), but worse tolerability (aOR 1.09; 99% CI 1.06, 1.13) and safety (aOR 1.12; 99% CI 1.08, 1.16). Observational and randomised data were similar for citalopram and sertraline, while there was some difference for drugs less prescribed in the real world., Conclusions: Antidepressants showed low acceptability, moderate-to-high tolerability and safety, and small-to-moderate effectiveness in the real world. Real-world and RCT estimates showed similar findings only when the analyses were carried out using large datasets; otherwise, the results diverged.

Background: Lung cancer is a leading cause of mortality, yet disparities in lung cancer across different sociodemographic groups in the UK remain unclear. This study investigates ethnicity and sociodemographic disparities and differences in lung cancer in a nationally representative English cohort, aiming to highlight inequalities and promote equitable access to diagnostic advancements., Methods: We conducted a population-based cohort study using health care records from QResearch, a large primary care database in England. The study included adults aged 25 and over, spanning the period of 2005-2019. Lung cancer incidence rates were calculated using age-standardized methods. Multinomial logistic regression was applied to assess associations between ethnicity/sociodemographic factors and diagnostic characteristics (histological type, stage, and cancer grade), adjusting for confounders., Findings: From a cohort of over 17.5 million people, we identified disparities in incidence rates across ethnic groups from 2005 to 2019. Analysis of 84,253 lung cancer cases revealed that younger woman and Individuals of Indian, other Asian, Black African, Caribbean and Chinese backgrounds had a significantly higher risks of adenocarcinoma compared with squamous cell carcinoma than their White counterparts (relative risk ratios [RRR] spanning from 1.52 (95% CI 1.18-1.94) to 2.69 (95% CI 1.43-5.05). Men and current smokers were more likely to be diagnosed at an advanced stage than women and never smokers (RRR: 1.72 [95% CI 1.56-1.90]-2.45 [95% CI 2.16-2.78]). Socioeconomic deprivation was associated with higher risks of moderate or poorly differentiated adenocarcinoma compared with well differentiated (RRRs between 1.35 [CI: 1.02-1.79] and 1.37 [1.05-1.80])., Interpretation: Our study highlights significant differences in lung cancer incidence and in lung cancer diagnostic characteristics related to ethnicity, deprivation and other demographic factors. These findings have important implications for the provision of equitable screening and prevention programmes to mitigate health inequalities., Funding: DART (The Integration and Analysis of Data using Artificial Intelligence to Improve Patient Outcomes with Thoracic Diseases) project, Innovate UK (UK Research and Innovation), QResearch® and grants from the NIHR Biomedical Research Centre (Oxford), John Fell Oxford University Press Research Fund, Cancer Research UK, and the Oxford Wellcome Institutional Strategic Support Fund., Competing Interests: JHC reports grants from National Institute for Health Research, John Fell Oxford University Press Research Fund, Cancer Research U.K. (C5255/A18085), 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. Until 09 August 2023, JHC had a 50% shareholding in ClinRisk Ltd, co-owning it with her husband, who was an executive director. On 9th August 2023, 100% of the share capital was donated to Endeavour Health Care Charitable Trust and the company renamed to Endeavour Predict Ltd. JHC is an unpaid consultant to Endeavour Predict Ltd and her husband is a non-executive director to cover the transition. The company licences software both to the private sector and to NHS bodies or bodies that provide services to the NHS (through GP electronic health record providers, pharmacies, hospital providers and other NHS providers). This software implements algorithms (including QRISK3) developed from access to the QResearch database during her time at the University of Nottingham. CC reports receiving personal fees from ClinRisk Ltd, outside this work. DRB reports receiving payments for speaking and advice for Astra Zeneca, Roche and MDS., (© 2024 Published by Elsevier Ltd.)

Background: Childhood, teenage and young adult (CTYA, 0-24 years) cancers are rare and diverse, making timely diagnosis challenging. We aim to explore symptoms and symptom combinations associated with a subsequent cancer diagnosis and to establish their timeframe., Methods: Using the QResearch Database, we carried out a matched nested case-control study. Associations between pre-specified symptoms encountered in primary care and a subsequent diagnosis of any cancer were explored using conditional logistic regression. Median diagnostic intervals were used to split symptoms into "late" and "early" timeframes to identify relevant early symptoms., Results: 3186 cases and 50,576 controls were identified from a cohort of 3,424,771 CTYA. We identified 12 novel associations, of which hemiparesis [OR 90.9 (95%CI 24.7-335.1), PPV = 1.6%], testicular swelling [OR 186.7 (95%CI 86.1-404.8), PPV = 2.4%] and organomegaly [OR 221.6 (95%CI 28.3-1735.9), PPV = 5.4%] had significant positive predictive values (PPV). Limb pain, a known marker of serious illness in children, was a recurrent early symptom across cancer subtypes. Similar clinical presentations were observed across childhood and TYA cancers., Discussion: Using the largest cohort to date, we provide novel information on the time-varying predictive utility of symptoms in the diagnosis of CTYA cancers. Our findings will help to raise clinical and public awareness of symptoms, stratify those at higher-risk and ultimately aid earlier diagnosis., (© 2024. The Author(s).)

Background: There is limited evidence on the safety of Hormone Replacement Therapy (HRT) in women with cancer. Therefore, we systematically examined HRT use and cancer-specific mortality in women with 17 site-specific cancers., Methods: Women newly diagnosed with 17 site-specific cancers from 1998 to 2019, were identified from general practitioner (GP) records, hospital diagnoses or cancer registries in Scotland, Wales and England. Breast cancer patients were excluded because HRT is contraindicated in breast cancer patients. The primary outcome was time to cancer-specific mortality. Time-dependent Cox regression models were used to calculate adjusted hazard ratios (HR) and 95% confidence intervals (95% CIs) for cancer-specific mortality by systemic HRT use., Results: The combined cancer cohorts contained 182,589 women across 17 cancer sites. Overall 7% of patients used systemic HRT after their cancer diagnosis. There was no evidence that HRT users, compared with non-users, had higher cancer-specific mortality at any cancer site. In particular, no increase was observed in common cancers including lung (adjusted HR = 0.98 95% CI 0.90, 1.07), colorectal (adjusted HR = 0.79 95% CI 0.70, 0.90), and melanoma (adjusted HR = 0.77 95% CI 0.58, 1.02)., Conclusions: We observed no evidence of increased cancer-specific mortality in women with a range of cancers (excluding breast) receiving HRT., (© 2024. The Author(s).)

Background: Dysmenorrhoea affects up to 94% of adolescents who menstruate; approximately one third miss school and activities. Dysmenorrhoea can occur without identified pelvic pathology (primary dysmenorrhoea) or in association with other conditions (secondary dysmenorrhoea). In adolescence, the commonest cause of secondary dysmenorrhoea is endometriosis. The incidence of symptoms in adolescence suggesting possible endometriosis has not been previously documented in GP records., Aim: To document incidence of adolescent endometriosis and symptoms associated with endometriosis in English GP records., Method: Data from the QResearch primary care database were used for adolescent females aged 10- 19 years between 1 January 2011 and 30 June 2021, reported using descriptive statistics., Results: The population cohort included 2 843 347 female adolescents; 98 887 participants had coded dysmenorrhoea (3.48%) and 1994 (0.07%) had documented endometriosis. The cumulative incidence for the cohort who turned 10 years old in 2011 was 7.2% for dysmenorrhoea and 0.12% for endometriosis. The period prevalence of coded symptoms during adolescence potentially associated with dysmenorrhoea and endometriosis includes: heavy menstrual bleeding (3.73%), irregular menstrual bleeding (2.21%), pelvic pain (0.63%), dyspareunia (0.40%), premenstrual syndrome (PMS)/premenstrual dysphoric disorder (PMDD) (0.22%), cystitis (8.45%), and irritable bowel syndrome (IBS) (1.00%). Disparities in coding were observed for these variables by ethnicity and socioeconomic status. Incidence of prescribed hormonal medication, with and without coded dysmenorrhoea, varied by ethnicity. This was less apparent for non-steroidal anti-inflammatory medications., Conclusion: Prevalence of coded dysmenorrhoea in GP records is significantly lower than community surveys suggest; however, adolescent menstrual symptoms are commonly encountered in primary care, and deserve specific guidance and resources. There are demographic patterns, likely structural, that warrant further exploration., (© British Journal of General Practice 2024.)

Background: Immunocompromised individuals are at increased risk of severe COVID-19 outcomes, underscoring the importance of COVID-19 vaccination in this population. The lack of comprehensive real-world data on vaccine uptake, effectiveness and safety in these individuals presents a critical knowledge gap, highlighting the urgency to better understand and address the unique challenges faced by immunocompromised individuals in the context of COVID-19 vaccination., Methods: We analysed data from 12,274,946 people in the UK aged > 12 years from 01/12/2020 to 11/04/2022. Of these, 583,541 (4.8%) were immunocompromised due to immunosuppressive drugs, organ transplants, dialysis or chemotherapy. We undertook a cohort analysis to determine COVID-19 vaccine uptake, nested case-control analyses adjusted for comorbidities and sociodemographic characteristics to determine effectiveness of vaccination against COVID-19 hospitalisation, ICU admission and death, and a self-controlled case series assessing vaccine safety for pre-specified adverse events of interest., Results: Overall, 93.7% of immunocompromised individuals received at least one COVID-19 vaccine dose, with 80.4% having received three or more doses. Uptake reduced with increasing deprivation (hazard ratio [HR] 0.78 [95%CI 0.77-0.79] in the most deprived quintile compared to the least deprived quintile for the first dose). Estimated vaccine effectiveness against COVID-19 hospitalisation 2-6 weeks after the second and third doses compared to unvaccinated was 78% (95%CI 72-83) and 91% (95%CI 88-93) in the immunocompromised population, versus 85% (95%CI 83-86) and 86% (95%CI 85-89), respectively, for the general population. Results showed COVID-19 vaccines were protective against intensive care unit (ICU) admission and death in both populations, with effectiveness of over 92% against COVID-19-related death and up to 95% in reducing ICU admissions for both populations following the third dose. COVID-19 vaccines were generally safe for immunocompromised individuals, though specific doses of ChAdOx1, mRNA-1273 and BNT162b2 raised risks of specific cardiovascular/neurological conditions., Conclusions: COVID-19 vaccine uptake is high in immunocompromised individuals on immunosuppressive drug therapy or who have undergone transplantation procedures, with documented disparities by deprivation. Findings suggest that COVID-19 vaccines are protective against severe COVID-19 outcomes in this vulnerable population, and show a similar safety profile in immunocompromised individuals and the general population, despite some increased risk of adverse events. These results underscore the importance of ongoing vaccination prioritisation for this clinically at-risk population to maximise protection against severe COVID-19 outcomes., (© 2024. The Author(s).)

Background: The National Institute for Health and Care Excellence (NICE) recommends that people aged 60+ years with newly diagnosed diabetes and weight loss undergo abdominal imaging to assess for pancreatic cancer. More nuanced stratification could lead to enrichment of these referral pathways., Methods: Population-based cohort study of adults aged 30-85 years at type 2 diabetes diagnosis (2010-2021) using the QResearch primary care database in England linked to secondary care data, the national cancer registry and mortality registers. Clinical prediction models were developed to estimate risks of pancreatic cancer diagnosis within 2 years and evaluated using internal-external cross-validation., Results: Seven hundred and sixty-seven of 253,766 individuals were diagnosed with pancreatic cancer within 2 years. Models included age, sex, BMI, prior venous thromboembolism, digoxin prescription, HbA1c, ALT, creatinine, haemoglobin, platelet count; and the presence of abdominal pain, weight loss, jaundice, heartburn, indigestion or nausea (previous 6 months). The Cox model had the highest discrimination (Harrell's C-index 0.802 (95% CI: 0.797-0.817)), the highest clinical utility, and was well calibrated. The model's highest 1% of predicted risks captured 12.51% of pancreatic cancer cases. NICE guidance had 3.95% sensitivity., Discussion: A new prediction model could have clinical utility in identifying individuals with recent onset diabetes suitable for fast-track abdominal imaging., (© 2024. The Author(s).)

The risk-benefit profile of COVID-19 vaccination in children remains uncertain. A self-controlled case-series study was conducted using linked data of 5.1 million children in England to compare risks of hospitalisation from vaccine safety outcomes after COVID-19 vaccination and infection. In 5-11-year-olds, we found no increased risks of adverse events 1-42 days following vaccination with BNT162b2, mRNA-1273 or ChAdOX1. In 12-17-year-olds, we estimated 3 (95%CI 0-5) and 5 (95%CI 3-6) additional cases of myocarditis per million following a first and second dose with BNT162b2, respectively. An additional 12 (95%CI 0-23) hospitalisations with epilepsy and 4 (95%CI 0-6) with demyelinating disease (in females only, mainly optic neuritis) were estimated per million following a second dose with BNT162b2. SARS-CoV-2 infection was associated with increased risks of hospitalisation from seven outcomes including multisystem inflammatory syndrome and myocarditis, but these risks were largely absent in those vaccinated prior to infection. We report a favourable safety profile of COVID-19 vaccination in under-18s., (© 2024. The Author(s).)

Purpose: Incidence of bleeding amongst warfarin and direct oral anticoagulant (DOAC) users is greater following a respiratory tract infection (RTI). It is unclear whether immediate antibiotics modify this association. We estimated the risk of bleeding amongst warfarin and DOAC users with RTI by antibiotic treatment., Methods: This retrospective cohort study used data from the Clinical Practice Research Datalink (CPRD) GOLD for adults in England prescribed warfarin or a DOAC, who sought primary care for an RTI between 1st January 2011 and 31st December 2019. Outcomes were major bleeding (hospital admission for intracranial or gastrointestinal bleeding), and non-major bleeding (hospital admission or General Practice consult for epistaxis, haemoptysis, or haematuria). Cox models derived hazard ratios (HRs) and 95% confidence intervals (CIs) for each outcome, adjusting for confounders using inverse probability of treatment weighting., Results: Of 14 817 warfarin and DOAC users consulting for an RTI, 8768 (59%) were prescribed immediate antibiotics and 6049 (41%) were not. Approximately 49% were female, and median age was 76 years. Antibiotics were associated with reduced risk of major bleeding (adjusted HR 0.38, 95% CI 0.25 to 0.58). This was consistent across several sensitivity analyses. Antibiotics were also associated with a reduced risk of non-major bleeding (adjusted HR 0.78, 95% CI 0.61 to 0.99)., Conclusions: Immediate antibiotics were associated with reduced risk of bleeding amongst warfarin and DOAC users with an RTI. Further work is needed to understand mechanisms and confirm whether a lower threshold for antibiotic use for RTI in this population may be beneficial., (© 2024 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons Ltd.)

QRISK algorithms use data from millions of people to help clinicians identify individuals at high risk of cardiovascular disease (CVD). Here, we derive and externally validate a new algorithm, which we have named QR4, that incorporates novel risk factors to estimate 10-year CVD risk separately for men and women. Health data from 9.98 million and 6.79 million adults from the United Kingdom were used for derivation and validation of the algorithm, respectively. Cause-specific Cox models were used to develop models to predict CVD risk, and the performance of QR4 was compared with version 3 of QRISK, Systematic Coronary Risk Evaluation 2 (SCORE2) and atherosclerotic cardiovascular disease (ASCVD) risk scores. We identified seven novel risk factors in models for both men and women (brain cancer, lung cancer, Down syndrome, blood cancer, chronic obstructive pulmonary disease, oral cancer and learning disability) and two additional novel risk factors in women (pre-eclampsia and postnatal depression). On external validation, QR4 had a higher C statistic than QRISK3 in both women (0.835 (95% confidence interval (CI), 0.833-0.837) and 0.831 (95% CI, 0.829-0.832) for QR4 and QRISK3, respectively) and men (0.814 (95% CI, 0.812-0.816) and 0.812 (95% CI, 0.810-0.814) for QR4 and QRISK3, respectively). QR4 was also more accurate than the ASCVD and SCORE2 risk scores in both men and women. The QR4 risk score identifies new risk groups and provides superior CVD risk prediction in the United Kingdom compared with other international scoring systems for CVD risk., (© 2024. The Author(s).)

Background: People with blood cancer have increased risk of severe COVID-19 outcomes and poor response to vaccination. We assessed the safety and effectiveness of COVID-19 vaccines in this vulnerable group compared to the general population., Methods: Individuals aged ≥12 years as of 1st December 2020 in the QResearch primary care database were included. We assessed adjusted COVID-19 vaccine effectiveness (aVE) against COVID-19-related hospitalisation and death in people with blood cancer using a nested matched case-control study. Using the self-controlled case series methodology, we compared the risk of 56 pre-specified adverse events within 1-28 days of a first, second or third COVID-19 vaccine dose in people with and without blood cancer., Findings: The cohort comprised 12,274,948 individuals, of whom 81,793 had blood cancer. COVID-19 vaccines were protective against COVID-19-related hospitalisation and death in people with blood cancer, although they were less effective, particularly against COVID-19-related hospitalisation, compared to the general population. In the blood cancer population, aVE against COVID-19-related hospitalisation was 64% (95% confidence interval [CI] 48%-75%) 14-41 days after a third dose, compared to 80% (95% CI 78%-81%) in the general population. Against COVID-19-related mortality, aVE was >80% in people with blood cancer 14-41 days after a second or third dose. We found no significant difference in risk of adverse events 1-28 days after any vaccine dose between people with and without blood cancer., Interpretation: Our study provides robust evidence which supports the use of COVID-19 vaccinations for people with blood cancer., Competing Interests: Declaration of Competing Interest This study is funded by Blood Cancer UK. JHC reports grants from 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 and other research councils, during the conduct of the study outside the scope of this work. JHC is founder and was shareholder until 9 Aug 2023 of ClinRisk Ltd, which produces open and closed source software to implement clinical risk algorithms (outside this work) into clinical computer systems. 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 an NIHR Senior Investigator outside the scope of this work. CC reports grants from NIHR and research councils outside the scope of this work during the past 36 months. All other authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objective: The QCovid 2 and 3 algorithms are risk prediction tools developed during the second wave of the COVID-19 pandemic that can be used to predict the risk of COVID-19 hospitalisation and mortality, taking vaccination status into account. In this study, we assess their performance in Scotland., Methods: We used the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 national data platform consisting of individual-level data for the population of Scotland (5.4 million residents). Primary care data were linked to reverse-transcription PCR virology testing, hospitalisation and mortality data. We assessed the discrimination and calibration of the QCovid 2 and 3 algorithms in predicting COVID-19 hospitalisations and deaths between 8 December 2020 and 15 June 2021., Results: Our validation dataset comprised 465 058 individuals, aged 19-100. We found the following performance metrics (95% CIs) for QCovid 2 and 3: Harrell's C 0.84 (0.82 to 0.86) for hospitalisation, and 0.92 (0.90 to 0.94) for death, observed-expected ratio of 0.24 for hospitalisation and 0.26 for death (ie, both the number of hospitalisations and the number of deaths were overestimated), and a Brier score of 0.0009 (0.00084 to 0.00096) for hospitalisation and 0.00036 (0.00032 to 0.0004) for death., Conclusions: We found good discrimination of the QCovid 2 and 3 algorithms in Scotland, although performance was worse in higher age groups. Both the number of hospitalisations and the number of deaths were overestimated., Competing Interests: Competing interests: JH-C reports grants from MRC, grants from Wellcome Trust, grants from NIHR, during the conduct of the study; 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 was 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. AS reports grants from NIHR, grants from MRC, grants from HDR UK, during the conduct of the study. All other authors report no conflict of interest., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

Background: Over time, the performance of clinical prediction models may deteriorate due to changes in clinical management, data quality, disease risk and/or patient mix. Such prediction models must be updated in order to remain useful. In this study, we investigate dynamic model updating of clinical survival prediction models. In contrast to discrete or one-time updating, dynamic updating refers to a repeated process for updating a prediction model with new data. We aim to extend previous research which focused largely on binary outcome prediction models by concentrating on time-to-event outcomes. We were motivated by the rapidly changing environment seen during the COVID-19 pandemic where mortality rates changed over time and new treatments and vaccines were introduced., Methods: We illustrate three methods for dynamic model updating: Bayesian dynamic updating, recalibration, and full refitting. We use a simulation study to compare performance in a range of scenarios including changing mortality rates, predictors with low prevalence and the introduction of a new treatment. Next, the updating strategies were applied to a model for predicting 70-day COVID-19-related mortality using patient data from QResearch, an electronic health records database from general practices in the UK., Results: In simulated scenarios with mortality rates changing over time, all updating methods resulted in better calibration than not updating. Moreover, dynamic updating outperformed ad hoc updating. In the simulation scenario with a new predictor and a small updating dataset, Bayesian updating improved the C-index over not updating and refitting. In the motivating example with a rare outcome, no single updating method offered the best performance., Conclusions: We found that a dynamic updating process outperformed one-time discrete updating in the simulations. Bayesian updating offered good performance overall, even in scenarios with new predictors and few events. Intercept recalibration was effective in scenarios with smaller sample size and changing baseline hazard. Refitting performance depended on sample size and produced abrupt changes in hazard ratio estimates between periods., (© 2023. The Author(s).)

Background: There is an increasing demand for mental health services for young people, which may vary across the year., Objective: To determine whether there are seasonal patterns in primary care antidepressant prescribing and mental health issues in adolescents and young adults., Methods: This cohort study used anonymised electronic health records from general practices in England contributing to QResearch. It included 5 081 263 males and females aged 14-18 (adolescents), 19-23 and 24-28 years between 2006 and 2019. The incidence rates per 1000 person-years and the incidence rate ratios (IRRs) were calculated for the first records of a selective serotonin reuptake inhibitor (SSRI) prescription, depression, anxiety and self-harm. The IRRs were adjusted for year, region, deprivation, ethnic group and number of working days., Findings: There was an increase in SSRI prescribing, depression and anxiety incidence in male and female adolescents in the autumn months (September-November) that was not seen in older age groups. The IRRs for SSRI prescribing for adolescents peaked in November (females: 1.75, 95% CI 1.67 to 1.83, p<0.001; males: 1.72, 95% CI 1.61 to 1.84, p<0.001, vs in January) and for depression (females: 1.29, 95% CI 1.25 to 1.33, p<0.001; males: 1.29, 95% CI 1.23 to 1.35, p<0.001). Anxiety peaked in November for females aged 14-18 years (1.17, 95% CI 1.13 to 1.22, p<0.001) and in September for males (1.19, 95% CI 1.12 to 1.27, p<0.001)., Conclusions: There were higher rates of antidepressant prescribing and consultations for depression and anxiety at the start of the school year among adolescents., Clinical Implications: Support around mental health issues from general practitioners and others should be focused during autumn., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

Background: Identifying female individuals at highest risk of developing life-threatening breast cancers could inform novel stratified early detection and prevention strategies to reduce breast cancer mortality, rather than only considering cancer incidence. We aimed to develop a prognostic model that accurately predicts the 10-year risk of breast cancer mortality in female individuals without breast cancer at baseline., Methods: In this model development and validation study, we used an open cohort study from the QResearch primary care database, which was linked to secondary care and national cancer and mortality registers in England, UK. The data extracted were from female individuals aged 20-90 years without previous breast cancer or ductal carcinoma in situ who entered the cohort between Jan 1, 2000, and Dec 31, 2020. The primary outcome was breast cancer-related death, which was assessed in the full dataset. Cox proportional hazards, competing risks regression, XGBoost, and neural network modelling approaches were used to predict the risk of breast cancer death within 10 years using routinely collected health-care data. Death due to causes other than breast cancer was the competing risk. Internal-external validation was used to evaluate prognostic model performance (using Harrell's C, calibration slope, and calibration in the large), performance heterogeneity, and transportability. Internal-external validation involved dataset partitioning by time period and geographical region. Decision curve analysis was used to assess clinical utility., Findings: We identified data for 11 626 969 female individuals, with 70 095 574 person-years of follow-up. There were 142 712 (1·2%) diagnoses of breast cancer, 24 043 (0·2%) breast cancer-related deaths, and 696 106 (6·0%) deaths from other causes. Meta-analysis pooled estimates of Harrell's C were highest for the competing risks model (0·932, 95% CI 0·917-0·946). The competing risks model was well calibrated overall (slope 1·011, 95% CI 0·978-1·044), and across different ethnic groups. Decision curve analysis suggested favourable clinical utility across all age groups. The XGBoost and neural network models had variable performance across age and ethnic groups., Interpretation: A model that predicts the combined risk of developing and then dying from breast cancer at the population level could inform stratified screening or chemoprevention strategies. Further evaluation of the competing risks model should comprise effect and health economic assessment of model-informed strategies., Funding: Cancer Research UK., Competing Interests: Declaration of interests SL reports institutional funding for clinical trials for which they are the Chief Investigator or Principal Investigator from Cancer Research UK, Boehringer Ingelheim, Piqur Therapeutics, AstraZeneca, Carrick Therapeutics, Sanofi, Merck, Synthon, Roche, and UK National Institute for Health and Care Research (NIHR); consulting fees from Sanofi, GLG Consulting, Rejuversen, Oxford Biodynamics; payment or honoraria for lectures or presentations from Esai, Prosigna, Roche, Pfizer, Novartis and Sanofi; personal support for attending meetings or for travel from Pfizer, Roche, Synthon and Piqur therapeutics; is a co-founder of MitoRx therapeutics; research funding from Cancer Research UK, Against Breast Cancer, Pathios Therapeutics, and NIHR; and participation on a data safety monitoring board or advisory board for Shionogi. JH-C is an unpaid director of QResearch and is a founder and shareholder of ClinRisk and was its medical director until May 31, 2019; and reports other grant support from the Wellcome Trust and the John Fell Fund. All other authors declare no competing interests., (Copyright © 2023 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: Lung cancer is the second most common cancer in incidence and the leading cause of cancer deaths worldwide. Meanwhile, lung cancer screening with low-dose CT can reduce mortality. The UK National Screening Committee recommended targeted lung cancer screening on Sept 29, 2022, and asked for more modelling work to be done to help refine the recommendation. This study aims to develop and validate a risk prediction model-the CanPredict (lung) model-for lung cancer screening in the UK and compare the model performance against seven other risk prediction models., Methods: For this retrospective, population-based, cohort study, we used linked electronic health records from two English primary care databases: QResearch (Jan 1, 2005-March 31, 2020) and Clinical Practice Research Datalink (CPRD) Gold (Jan 1, 2004-Jan 1, 2015). The primary study outcome was an incident diagnosis of lung cancer. We used a Cox proportional-hazards model in the derivation cohort (12·99 million individuals aged 25-84 years from the QResearch database) to develop the CanPredict (lung) model in men and women. We used discrimination measures (Harrell's C statistic, D statistic, and the explained variation in time to diagnosis of lung cancer [R 2 D ]) and calibration plots to evaluate model performance by sex and ethnicity, using data from QResearch (4·14 million people for internal validation) and CPRD (2·54 million for external validation). Seven models for predicting lung cancer risk (Liverpool Lung Project [LLP] v2 , LLP v3 , Lung Cancer Risk Assessment Tool [LCRAT], Prostate, Lung, Colorectal, and Ovarian [PLCO] M2012 , PLCO M2014 , Pittsburgh, and Bach) were selected to compare their model performance with the CanPredict (lung) model using two approaches: (1) in ever-smokers aged 55-74 years (the population recommended for lung cancer screening in the UK), and (2) in the populations for each model determined by that model's eligibility criteria., Findings: There were 73 380 incident lung cancer cases in the QResearch derivation cohort, 22 838 cases in the QResearch internal validation cohort, and 16 145 cases in the CPRD external validation cohort during follow-up. The predictors in the final model included sociodemographic characteristics (age, sex, ethnicity, Townsend score), lifestyle factors (BMI, smoking and alcohol status), comorbidities, family history of lung cancer, and personal history of other cancers. Some predictors were different between the models for women and men, but model performance was similar between sexes. The CanPredict (lung) model showed excellent discrimination and calibration in both internal and external validation of the full model, by sex and ethnicity. The model explained 65% of the variation in time to diagnosis of lung cancer R 2 D in both sexes in the QResearch validation cohort and 59% of the R 2 D in both sexes in the CPRD validation cohort. Harrell's C statistics were 0·90 in the QResearch (validation) cohort and 0·87 in the CPRD cohort, and the D statistics were 2·8 in the QResearch (validation) cohort and 2·4 in the CPRD cohort. Compared with seven other lung cancer prediction models, the CanPredict (lung) model had the best performance in discrimination, calibration, and net benefit across three prediction horizons (5, 6, and 10 years) in the two approaches. The CanPredict (lung) model also had higher sensitivity than the current UK recommended models (LLP v2 and PLCO M2012 ), as it identified more lung cancer cases than those models by screening the same amount of individuals at high risk., Interpretation: The CanPredict (lung) model was developed, and internally and externally validated, using data from 19·67 million people from two English primary care databases. Our model has potential utility for risk stratification of the UK primary care population and selection of individuals at high risk of lung cancer for targeted screening. If our model is recommended to be implemented in primary care, each individual's risk can be calculated using information in the primary care electronic health records, and people at high risk can be identified for the lung cancer screening programme., Funding: Innovate UK (UK Research and Innovation)., Translation: For the Chinese translation of the abstract see Supplementary Materials section., Competing Interests: Declaration of interests JH-C is an unpaid director of QResearch, a not-for-profit organisation in a partnership between the University of Oxford and EMIS Health, who supply the QResearch database for this work. JH-C is also a founder and shareholder of ClinRisk, who produce open-source and closed-source software to implement clinical risk algorithms, and was its medical director until May 31, 2019. FVG is a shareholder of Optellums Ltd, an AI company that produces diagnostic algorithms for nodules on CT scans, mainly in lung cancer, and received honoraria from Roche. But these are unrelated to this study. DRB received honoraria from Astra Zeneca, Roche, Bristol Myers Squibb, and MSD, which is not related to this study. CACC received payment from previous consultancy with ClinRisk Ltd, which is outside of the current work. WL and JB have no interests to declare., (Copyright © 2023 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.)

Objectives: To derive and validate risk prediction algorithms (QCOVID4) to estimate the risk of covid-19 related death and hospital admission in people with a positive SARS-CoV-2 test result during the period when the omicron variant of the virus was predominant in England, and to evaluate performance compared with a high risk cohort from NHS Digital., Design: Cohort study., Setting: QResearch database linked to English national data on covid-19 vaccinations, SARS-CoV-2 test results, hospital admissions, and cancer and mortality data, 11 December 2021 to 31 March 2022, with follow-up to 30 June 2022., Participants: 1.3 million adults in the derivation cohort and 0.15 million adults in the validation cohort, aged 18-100 years, with a positive test result for SARS-CoV-2 infection., Main Outcome Measures: Primary outcome was covid-19 related death and secondary outcome was hospital admission for covid-19. Risk equations with predictor variables were derived from models fitted in the derivation cohort. Performance was evaluated in a separate validation cohort., Results: Of 1 297 922 people with a positive test result for SARS-CoV-2 infection in the derivation cohort, 18 756 (1.5%) had a covid-19 related hospital admission and 3878 (0.3%) had a covid-19 related death during follow-up. The final QCOVID4 models included age, deprivation score and a range of health and sociodemographic factors, number of covid-19 vaccinations, and previous SARS-CoV-2 infection. The risk of death related to covid-19 was lower among those who had received a covid-19 vaccine, with evidence of a dose-response relation (42% risk reduction associated with one vaccine dose and 92% reduction with four or more doses in men). Previous SARS-CoV-2 infection was associated with a reduction in the risk of covid-19 related death (49% reduction in men). The QCOVID4 algorithm for covid-19 explained 76.0% (95% confidence interval 73.9% to 78.2%) of the variation in time to covid-19 related death in men with a D statistic of 3.65 (3.43 to 3.86) and Harrell's C statistic of 0.970 (0.962 to 0.979). Results were similar for women. QCOVID4 was well calibrated. QCOVID4 was substantially more efficient than the NHS Digital algorithm for correctly identifying patients at high risk of covid-19 related death. Of the 461 covid-19 related deaths in the validation cohort, 333 (72.2%) were in the QCOVID4 high risk group and 95 (20.6%) in the NHS Digital high risk group., Conclusion: The QCOVID4 risk algorithm, modelled from data during the period when the omicron variant of the SARS-CoV-2 virus was predominant in England, now includes vaccination dose and previous SARS-CoV-2 infection, and predicted covid-19 related death among people with a positive test result. QCOVID4 more accurately identified individuals at the highest levels of absolute risk for targeted interventions than the approach adopted by NHS Digital. QCOVID4 performed well and could be used for targeting treatments for covid-19 disease., 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 National Institute for Health and Care Research (NIHR), the John Fell Oxford University Press Research Fund, Cancer Research UK, and Oxford Wellcome Institutional Strategic Support Fund for the submitted work; JH-C reports grants from NIHR Biomedical Research Centre, Oxford, John Fell Oxford University Press Research Fund, Cancer Research UK, through the Cancer Research UK Oxford Centre, 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 which is a partnership between the University of Oxford and EMIS Health who 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 was chair of the NERVTAG (New and Emerging Respiratory Virus Threats Advisory Group) risk stratification subgroup and a member of SAGE (Scientific Advisory Group for Emergencies) 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 was a member of the NERVTAG risk stratification subgroup; KK is supported by NIHR Applied Research Collaboration East Midlands (ARC-EM) and the NIHR Biomedical Research Centre; KK is chair of the ethnic subgroup of UK SAGE and a member of SAGE; AS has received research grants from NIHR, Medical Research Council, Health Data Research UK, Central Statistics Office, National Childcare Scheme, and GSK for covid-19 work, and has provided advice for AstraZeneca’s Thrombotic Thrombocytopenic Taskforce and the UK and Scottish Government covid-19 advisory groups (roles unremunerated); no financial relationships with any organizations 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.)

Introduction: At the start of the COVID-19 pandemic there was an urgent need to identify individuals at highest risk of severe outcomes, such as hospitalisation and death following infection. The QCOVID risk prediction algorithms emerged as key tools in facilitating this which were further developed during the second wave of the COVID-19 pandemic to identify groups of people at highest risk of severe COVID-19 related outcomes following one or two doses of vaccine., Objectives: To externally validate the QCOVID3 algorithm based on primary and secondary care records for Wales, UK., Methods: We conducted an observational, prospective cohort based on electronic health care records for 1.66m vaccinated adults living in Wales on 8th December 2020, with follow-up until 15th June 2021. Follow-up started from day 14 post vaccination to allow the full effect of the vaccine., Results: The scores produced by the QCOVID3 risk algorithm showed high levels of discrimination for both COVID-19 related deaths and hospital admissions and good calibration (Harrell C statistic: ≥ 0.828)., Conclusion: This validation of the updated QCOVID3 risk algorithms in the adult vaccinated Welsh population has shown that the algorithms are valid for use in the Welsh population, and applicable on a population independent of the original study, which has not been previously reported. This study provides further evidence that the QCOVID algorithms can help inform public health risk management on the ongoing surveillance and intervention to manage COVID-19 related risks., Competing Interests: KK is Chair of the Ethnicity Subgroup of the UK Scientific Advisory Group for Emergencies (SAGE) and Member of SAGE. AS is a member of the Scottish Government Chief Medical Officer’s COVID-19 Advisory Group, the Scottish Government’s Standing Committee on Pandemic Preparedness, the UK Government’s New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) Risk Stratification Subgroup, the Department of Health and Social Care’s COVID-19 Therapeutics Modelling Group and was a member of Astra-Zeneca’s COVID-19 Strategic Thrombocytopenia Taskforce. All AS’ roles are unfunded. JHC is a member of SAGE subgroups and chair of the NERVTAG subgroup on risk stratification and several DHSC therapeutics modelling advisory groups. RAL is a member of the Welsh Government COVID-19 Technical Advisory Group., (Copyright: © 2023 Lyons et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Liver cancer has one of the fastest rising incidence and mortality rates among all cancers in the UK, but it receives little attention. This study aims to understand the disparities in epidemiology and clinical pathways of primary liver cancer and identify the gaps for early detection and diagnosis of liver cancer in England., Methods: This study used a dynamic English primary care cohort of 8.52 million individuals aged ≥25 years in the QResearch database during 2008-2018, followed up to June 2021. The crude and age-standardised incidence rates, and the observed survival duration were calculated by sex and three liver cancer subtypes, including hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (CCA), and other specified/unspecified primary liver cancer. Regression models were used to investigate factors associated with an incident diagnosis of liver cancer, emergency presentation, late stage at diagnosis, receiving treatments, and survival duration after diagnosis by subtype., Findings: 7331 patients were diagnosed with primary liver cancer during follow-up. The age-standardised incidence rates increased over the study period, particularly for HCC in men (increased by 60%). Age, sex, socioeconomic deprivation, ethnicity, and geographical regions were all significantly associated with liver cancer incidence in the English primary care population. People aged ≥80 years were more likely to be diagnosed through emergency presentation and in late stages, less likely to receive treatments and had poorer survival than those aged <60 years. Men had a higher risk of being diagnosed with liver cancer than women, with a hazard ratio (HR) of 3.9 (95% confidence interval 3.6-4.2) for HCC, 1.2 (1.1-1.3) for CCA, and 1.7 (1.5-2.0) for other specified/unspecified liver cancer. Compared with white British, Asians and Black Africans were more likely to be diagnosed with HCC. Patients with higher socioeconomic deprivation were more likely to be diagnosed through the emergency route. Survival rates were poor overall. Patients diagnosed with HCC had better survival rates (14.5% at 10-year survival, 13.1%-16.0%) compared to CCA (4.4%, 3.4%-5.6%) and other specified/unspecified liver cancer (12.5%, 10.1%-15.2%). For 62.7% of patients with missing/unknown stage in liver cancer, their survival outcomes were between those diagnosed in Stages III and IV., Interpretation: This study provides an overview of the current epidemiology and the disparities in clinical pathways of primary liver cancer in England between 2008 and 2018. A complex public health approach is needed to tackle the rapid increase in incidence and the poor survival of liver cancer. Further studies are urgently needed to address the gaps in early detection and diagnosis of liver cancer in England., Funding: The Early Detection of Hepatocellular Liver Cancer (DeLIVER) project is funded by Cancer Research UK (Early Detection Programme Award, grant reference: C30358/A29725)., Competing Interests: JH-C is an unpaid director of QResearch, a not-for-profit organisation in a partnership between the University of Oxford and EMIS Health, who supply the QResearch database for this work. JH-C 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 into clinical computer systems. EB contributed to patents on imaging technologies that are owned by Perspectum Diagnostics, an imaging spin-out company of the University of Oxford, and holds shares of Perspectum Diagnostics. EB received honoraria from Roche Diagnostics for a presentation at a symposium and for contributions/evaluations of a manuscript. PJ received research funding from Novo Nordisk Foundation as a Borregaard Clinical Ascending Investigator (grant reference number NNF19OC0054612). The funder had no role in this study. The University of Oxford received funding from GSK, which partly contributed to a DPhil studentship for CC in HBV and HCC epidemiology. The DeLIVER consortium has Roche, Perspectum Diagnostics, and Oncimmune as industry partners. WL, CACC, and HI have no interests to declare for this work., (© 2023 University of Oxford.)

Objective: To develop a clinically useful model that estimates the 10 year risk of breast cancer related mortality in women (self-reported female sex) with breast cancer of any stage, comparing results from regression and machine learning approaches., Design: Population based cohort study., Setting: QResearch primary care database in England, with individual level linkage to the national cancer registry, Hospital Episodes Statistics, and national mortality registers., Participants: 141 765 women aged 20 years and older with a diagnosis of invasive breast cancer between 1 January 2000 and 31 December 2020., Main Outcome Measures: Four model building strategies comprising two regression (Cox proportional hazards and competing risks regression) and two machine learning (XGBoost and an artificial neural network) approaches. Internal-external cross validation was used for model evaluation. Random effects meta-analysis that pooled estimates of discrimination and calibration metrics, calibration plots, and decision curve analysis were used to assess model performance, transportability, and clinical utility., Results: During a median 4.16 years (interquartile range 1.76-8.26) of follow-up, 21 688 breast cancer related deaths and 11 454 deaths from other causes occurred. Restricting to 10 years maximum follow-up from breast cancer diagnosis, 20 367 breast cancer related deaths occurred during a total of 688 564.81 person years. The crude breast cancer mortality rate was 295.79 per 10 000 person years (95% confidence interval 291.75 to 299.88). Predictors varied for each regression model, but both Cox and competing risks models included age at diagnosis, body mass index, smoking status, route to diagnosis, hormone receptor status, cancer stage, and grade of breast cancer. The Cox model's random effects meta-analysis pooled estimate for Harrell's C index was the highest of any model at 0.858 (95% confidence interval 0.853 to 0.864, and 95% prediction interval 0.843 to 0.873). It appeared acceptably calibrated on calibration plots. The competing risks regression model had good discrimination: pooled Harrell's C index 0.849 (0.839 to 0.859, and 0.821 to 0.876, and evidence of systematic miscalibration on summary metrics was lacking. The machine learning models had acceptable discrimination overall (Harrell's C index: XGBoost 0.821 (0.813 to 0.828, and 0.805 to 0.837); neural network 0.847 (0.835 to 0.858, and 0.816 to 0.878)), but had more complex patterns of miscalibration and more variable regional and stage specific performance. Decision curve analysis suggested that the Cox and competing risks regression models tested may have higher clinical utility than the two machine learning approaches., Conclusion: In women with breast cancer of any stage, using the predictors available in this dataset, regression based methods had better and more consistent performance compared with machine learning approaches and may be worthy of further evaluation for potential clinical use, such as for stratified follow-up., Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: JHC is an unpaid director of QResearch (a not-for-profit organisation that is a partnership between the University of Oxford and EMIS Health that 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 two breast cancer risk models implemented in the National Health Service into clinical computer systems., (© 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: People with blood cancers have increased risk of severe outcomes from COVID-19 and were prioritised for vaccination., Methods: Individuals in the QResearch database aged 12 years and above on 1st December 2020 were included in the analysis. Kaplan-Meier analysis described time to COVID-19 vaccine uptake in people with blood cancer and other high-risk disorders. Cox regression was used to identify factors associated with vaccine uptake in people with blood cancer., Results: The analysis included 12,274,948 individuals, of whom 97,707 had a blood cancer diagnosis. 92% of people with blood cancer received at least one dose of vaccine, compared to 80% of the general population, but there was lower uptake of each subsequent vaccine dose (31% for fourth dose). Vaccine uptake decreased with social deprivation (HR 0.72, 95% CI 0.70, 0.74 for most deprived versus most affluent quintile for first vaccine). Compared with White groups, uptake of all vaccine doses was significantly lower in people of Pakistani and Black ethnicity, and more people in these groups remain unvaccinated., Conclusions: COVID-19 vaccine uptake declines following second dose and there are ethnic and social disparities in uptake in blood cancer populations. Enhanced communication of benefits of vaccination to these groups is needed., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Concerns have been raised that angiotensin-converting enzyme-inhibitors (ACE-I) and angiotensin receptor blockers (ARBs) might facilitate transmission of severe acute respiratory syndrome coronavirus 2 leading to more severe coronavirus disease (COVID-19) disease and an increased risk of mortality. We aimed to investigate the association between ACE-I/ARB treatment and risk of death amongst people with COVID-19 in the first 6 months of the pandemic., Methods: We identified a cohort of adults diagnosed with either confirmed or probable COVID-19 (from 1 January to 21 June 2020) using computerized medical records from the Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) primary care database. This comprised 465 general practices in England, United Kingdom with a nationally representative population of 3.7 million people. We constructed mixed-effects logistic regression models to quantify the association between ACE-I/ARBs and all-cause mortality among people with COVID-19, adjusted for sociodemographic factors, comorbidities, concurrent medication, smoking status, practice clustering, and household number., Results: There were 9,586 COVID-19 cases in the sample and 1,463 (15.3%) died during the study period between 1 January 2020 and 21 June 2020. In adjusted analysis ACE-I and ARBs were not associated with all-cause mortality (adjusted odds ratio [OR] 1.02, 95% confidence interval [CI] 0.85-1.21 and OR 0.84, 95% CI 0.67-1.07, respectively)., Conclusion: Use of ACE-I/ARB, which are commonly used drugs, did not alter the odds of all-cause mortality amongst people diagnosed with COVID-19. Our findings should inform patient and prescriber decisions concerning continued use of these medications during the pandemic., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objectives: Uptake of influenza, pneumococcal and shingles vaccines in older adults vary across regions and socioeconomic backgrounds. In this study, we study the coverage and factors associated with vaccination uptake, as well as refusal in the unvaccinated population and their associations with ethnicity, deprivation, household size and health conditions., Design, Setting and Participants: This is a cross-sectional study of adults aged 65 years or older in England, using a large primary care database. Associations of vaccine uptake and refusal in the unvaccinated with ethnicity, deprivation, household size and health conditions were modelled using multivariable logistic regression., Outcome Measure: Influenza, pneumococcal and shingles vaccine uptake and refusal (in the unvaccinated)., Results: This study included 2 054 463 patients from 1318 general practices. 1 711 465 (83.3%) received at least one influenza vaccine, 1 391 228 (67.7%) pneumococcal vaccine and 690 783 (53.4%) shingles vaccine. Compared with White ethnicity, influenza vaccine uptake was lower in Chinese (OR 0.49; 95% CI 0.45 to 0.53), 'Other ethnic' groups (0.63; 95% CI 0.60 to 0.65), black Caribbean (0.68; 95% CI 0.64 to 0.71) and black African (0.72; 95% CI 0.68 to 0.77). There was generally lower vaccination uptake among more deprived individuals, people living in larger household sizes (three or more persons) and those with fewer health conditions. Among those who were unvaccinated, higher odds of refusal were associated with the black Caribbean ethnic group and marginally with increased deprivation, but not associated with higher refusal in those living in large households or those with lesser health conditions., Conclusion: Certain ethnic minority groups, deprived populations, large households and 'healthier' individuals were less likely to receive a vaccine, although higher refusal was only associated with ethnicity and deprivation but not larger households nor healthier individuals. Understanding these may inform tailored public health messaging to different communities for equitable implementation of vaccination programmes., Competing Interests: Competing interests: PST reports previous consultation with AstraZeneca and Duke-NUS outside the submitted work. KK is a Member of the Scientific Advisory Group for Emergencies (SAGE), Member of Independent SAGE, Director of the University of Leicester Centre for Black Minority Health and Trustee of the south Asian Health Foundation. JH-C is a member of several SAGE committees and chair of the risk stratification subgroup of the NERVTAG. She is an unpaid director of QResearch and founder and former medical director of ClinRisk Ltd (outside the submitted work). MP, AKC, HD-M, DS, TAR, FZ, BRS, SJG, CC, CG have no interests to declare., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

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 aimed to i) extract and summarise existing national HBV prevalence estimates, ii) add a new estimate based on primary care data, and; iii) critique data sources from which estimates were derived. Methods: We undertook a narrative review, searching for national estimates of CHB case numbers in the UK (incorporating incidence, prevalence and/or test positivity data) across a range of overlapping sources, including governmental body reports, publications from independent bodies (including medical charities and non-governmental organisations) and articles in peer-reviewed scientific journals.  An alternative proxy for population prevalence was obtained via the UK antenatal screening programme which achieves over 95% coverage of pregnant women. We also searched for diagnoses of HBV in the QResearch primary care database based on laboratory tests and standardised coding. 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%. Our estimate, based on QResearch data, suggests a population prevalence of ~0.05%, reflecting a substantial underestimation based on primary care records. Discussion : Estimates varied by sources of error, bias and missingness, data linkage, and "blind spots" in HBV diagnoses testing/registration. The UK HBV burden is likely to be concentrated in vulnerable populations who may not be well represented in existing datasets including those experiencing socioeconomic deprivation and/or homelessness, ethnic minorities and people born in high-prevalence countries. This could lead to under- or over-estimation of population prevalence estimation. Multi-agency collaboration is required to fill evidence gaps., 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: © 2023 Campbell C et al.)