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Randomised controlled trials (RCTs) inform prescription guidelines, but stringent eligibility criteria exclude individuals with vulnerable characteristics, which we define as comorbidities, concomitant medication use, and vulnerabilities due to age. Poor external validity can result in inadequate treatment decision information. Our first aim was to quantify the extent of exclusion of individuals with vulnerable characteristics from RCTs for all prescription drugs. Our second aim was to quantify the prevalence of individuals with vulnerable characteristics from population electronic health records who are actively prescribed such drugs. In tandem, these two aims will allow us to assess the representativeness between RCT and real-world populations and identify vulnerable populations potentially at risk of inadequate treatment decision information. When a vulnerable population is highly excluded from RCTs but has a high prevalence of individuals actively being prescribed the same medication, there is likely to be a gap in treatment decision information. Our third aim was to investigate the use of real-world evidence in contributing towards quantifying missing treatment risk or benefit through an observational study.We extracted RCTs from ClinicalTrials.gov from its inception to April 28, 2021, and primary care records from the Clinical Practice Research Datalink Gold database from Jan 1, 1998, to Dec 31, 2020. We referred to the British National Formulary to classify prescription drugs into drug categories. We conducted descriptive analyses and quantified RCT exclusion and prevalence of individuals with vulnerable characteristics for comparison to identify populations without treatment decision information. Exclusion and prevalence were assessed separately for different age groups, individual clinical specialities, and for quantities of concomitant conditions by clinical specialities, where multimorbidity was defined as having two or more clinical specialties, and medications prescribed, where polypharmacy was defined as having five or more medications prescribed. Population trends of individuals with multimorbidity or polypharmacy were assessed separately by age group. We conducted an observational cohort study to validate the use of real-world evidence in contributing towards quantifying treatment risk or benefit for patients with dementia on anti-dementia drugs with and without a contraindicated clinical speciality. To do so, we identified the clinical specialities that anti-dementia drug RCTs highly excluded yet had corresponding high prevalence in the real-world population, forming the groups with highest risk of having scarce treatment decision information. Cox regression was used to assess if the risk of mortality outcomes differs between both groups.43 895 RCTs from ClinicalTrials.gov and 5 685 738 million individuals from primary care records were used. We considered 989 unique drugs and 286 conditions across 13 drug-category cohorts. For the descriptive analyses, the median RCT exclusion proportion across 13 drug categories was 81·5% (IQR 76·7-85·5) for adolescents (aged18 years), 26·3% (IQR 21·0-29·5) for individuals older than 60 years, 40·5% (IQR 33·7-43·0) for individuals older than 70 years, and 52·9% (IQR 47·1-56·0) for individuals older than 80 years. Multimorbidity had a median exclusion proportion of 91·1% (IQR 88·9-91·8) and median prevalence of 41·0% (IQR 34·9-46·0). Concomitant medication use had a median exclusion proportion of 52·5% (IQR 50·0-53·7) and a median prevalence of 94·3% (IQR 84·3-97·2), and polypharmacy had a median prevalence of 47·7% (IQR 38·0-56·1). Population trends show increasing multimorbidity with age and consistently high polypharmacy across age groups. Populations with cardiovascular or otorhinolaryngological comorbidities had the highest risk of having scarce treatment decision information. For the observational study, populations with cardiovascular or psychiatric comorbidities had highest risk of having scarce treatment decision information. Patients with dementia with an anti-dementia prescription and contraindicated cardiovascular condition had a higher risk of mortality (hazard ratio [HR] 1·20 [95% CI 1·13-1·28 ; p0·0001]) compared with patients with dementia without a contraindicated cardiovascular condition. Patients with dementia with comorbid delirium (HR 1·25 [95% CI 1·06-1·48]; p0·0088), intellectual disability (HR 2·72 [95% CI 1·53-4·81]; p=0·0006), and schizophrenia and schizotypal delusional disorders (HR 1·36 [95% CI 1·02-1·82]; p=0·036) had a higher risk of mortality compared with patients with dementia without these conditions.Overly stringent RCT exclusion criteria do not appropriately account for the heterogeneity of vulnerable characteristics observed in real-world populations. Treatment decision information is scarce for such individuals, which might affect health outcomes. We discuss the challenges facing the inclusivity of such individuals and highlight the strength of real-world evidence as an integrative solution in complementing RCTs and increasing the completeness of evidence-based medicine assessments in evaluating the effectiveness of treatment decisions.Wellcome Trust, National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre, NIHR Great Ormond Street Hospital Biomedical Research Centre, Academy of Medical Sciences, and the University College London Overseas Research Scholarship.

BACKGROUND: Globally, there is a paucity of multimorbidity and comorbidity data, especially for minority ethnic groups and younger people. We estimated the frequency of common disease combinations and identified non-random disease associations for all ages in a multiethnic population. METHODS: In this population-based study, we examined multimorbidity and comorbidity patterns stratified by ethnicity or race, sex, and age for 308 health conditions using electronic health records from individuals included on the Clinical Practice Research Datalink linked with the Hospital Episode Statistics admitted patient care dataset in England. We included individuals who were older than 1 year and who had been registered for at least 1 year in a participating general practice during the study period (between April 1, 2010, and March 31, 2015). We identified the most common combinations of conditions and comorbidities for index conditions. We defined comorbidity as the accumulation of additional conditions to an index condition over an individual's lifetime. We used network analysis to identify conditions that co-occurred more often than expected by chance. We developed online interactive tools to explore multimorbidity and comorbidity patterns overall and by subgroup based on ethnicity, sex, and age. FINDINGS: We collected data for 3 872 451 eligible patients, of whom 1 955 700 (50.5%) were women and girls, 1 916 751 (49.5%) were men and boys, 2 666 234 (68.9%) were White, 155 435 (4.0%) were south Asian, and 98 815 (2.6%) were Black. We found that a higher proportion of boys aged 1-9 years (132 506 [47.8%] of 277 158) had two or more diagnosed conditions than did girls in the same age group (106 982 [40.3%] of 265 179), but more women and girls were diagnosed with multimorbidity than were boys aged 10 years and older and men (1 361 232 [80.5%] of 1 690 521 vs 1 161 308 [70.8%] of 1 639 593). White individuals (2 097 536 [78.7%] of 2 666 234) were more likely to be diagnosed with two or more conditions than were Black (59 339 [60.1%] of 98 815) or south Asian individuals (93 617 [60.2%] of 155 435). Depression commonly co-occurred with anxiety, migraine, obesity, atopic conditions, deafness, soft-tissue disorders, and gastrointestinal disorders across all subgroups. Heart failure often co-occurred with hypertension, atrial fibrillation, osteoarthritis, stable angina, myocardial infarction, chronic kidney disease, type 2 diabetes, and chronic obstructive pulmonary disease. Spinal fractures were most strongly non-randomly associated with malignancy in Black individuals, but with osteoporosis in White individuals. Hypertension was most strongly associated with kidney disorders in those aged 20-29 years, but with dyslipidaemia, obesity, and type 2 diabetes in individuals aged 40 years and older. Breast cancer was associated with different comorbidities in individuals from different ethnic groups. Asthma was associated with different comorbidities between males and females. Bipolar disorder was associated with different comorbidities in younger age groups compared with older age groups. INTERPRETATION: Our findings and interactive online tools are a resource for: patients and their clinicians, to prevent and detect comorbid conditions; research funders and policy makers, to redesign service provision, training priorities, and guideline development; and biomedical researchers and manufacturers of medicines, to provide leads for research into common or sequential pathways of disease and inform the design of clinical trials. FUNDING: UK Research and Innovation, Medical Research Council, National Institute for Health and Care Research, Department of Health and Social Care, Wellcome Trust, British Heart Foundation, and The Alan Turing Institute.

Comorbidities present considerable challenges to cancer treatment and care. However, little is known about the effect of comorbidity on cancer treatment decisions across a wide range of cancer types and treatment modalities. Harnessing a cohort of 280,543 patients spanning 19 site-specific cancers, we explored pan-cancer frequencies of 109 comorbidities. Multinomial regression revealed that patients with comorbidity exhibited lower odds of receiving chemotherapy and multimodality treatment. End-stage renal disease was significantly associated with a decreased odds of receiving chemotherapy and surgery. Patients with prostate cancer who have comorbid non-acute cystitis, obstructive and reflux uropathy, urolithiasis, or hypertension were less likely to receive chemotherapy. Among patients with breast cancer, dementia, left bundle branch block, peripheral arterial disease, epilepsy, Barrett’s oesophagus, ischaemic stroke, unstable angina and asthma were associated with lower odds of receiving multimodal chemotherapy, radiotherapy and surgery. Comorbidity is also consistently associated with the lower odds of receiving chemotherapy when comparing across 10 drug classes. Patients with comorbid dementia, intracerebral haemorrhage, subarachnoid haemorrhage, oesophageal varices, liver fibrosis sclerosis and cirrhosis and secondary pulmonary hypertension were less likely to receive antimetabolites. Comorbidity can influence the effectiveness and tolerability of cancer treatment and ultimately, prognosis. Multi-specialty collaborative care is essential for the management of comorbidity during cancer treatment, including prophylactic measures to manage toxicities.

Objectives Obesity is a modifiable risk factor for coronavirus disease 2019 (COVID-19)–related mortality. We estimated excess mortality in obesity, both ‘direct’, through infection, and ‘indirect’, through changes in health care, and also due to potential increasing obesity during lockdown. Study design The study design of this study is a retrospective cohort study and causal inference methods. Methods In population-based electronic health records for 1,958,638 individuals in England, we estimated 1-year mortality risk (‘direct’ and ‘indirect’ effects) for obese individuals, incorporating (i) pre-COVID-19 risk by age, sex and comorbidities, (ii) population infection rate and (iii) relative impact on mortality (relative risk [RR]: 1.2, 1.5, 2.0 and 3.0). Using causal inference models, we estimated impact of change in body mass index (BMI) and physical activity during 3-month lockdown on 1-year incidence for high-risk conditions (cardiovascular diseases, diabetes, chronic obstructive pulmonary disease and chronic kidney disease), accounting for confounders. Results For severely obese individuals (3.5% at baseline), at 10% population infection rate, we estimated direct impact of 240 and 479 excess deaths in England at RR 1.5 and 2.0, respectively, and indirect effect of 383–767 excess deaths, assuming 40% and 80% will be affected at RR = 1.2. Owing to BMI change during the lockdown, we estimated that 97,755 (5.4%: normal weight to overweight, 5.0%: overweight to obese and 1.3%: obese to severely obese) to 434,104 individuals (15%: normal weight to overweight, 15%: overweight to obese and 6%: obese to severely obese) would be at higher risk for COVID-19 over one year. Conclusions Prevention of obesity and promotion of physical activity are at least as important as physical isolation of severely obese individuals during the pandemic., Highlights • In severely obese individuals, we estimated a direct impact of 240–479 excess deaths in England and indirect effect of 383–767 excess deaths over 1 year, assuming 10% infection rate. • Between 97 755 and 434 104 individuals may develop high-risk conditions for coronavirus disease 2019 (COVID-19) mortality during a 3-month lockdown due to change in body mass index and physical activity. • These analyses support COVID-19 and non-COVID-19 impact assessment in policy planning. • The implications of distancing and isolation measures on incidence and mortality from chronic diseases, particularly relating to obesity, need to be considered in clinical practice, public health and research.

Aims Cardiovascular diseases (CVDs) increase mortality risk from coronavirus infection (COVID-19). There are also concerns that the pandemic has affected supply and demand of acute cardiovascular care. We estimated excess mortality in specific CVDs, both ‘direct’, through infection, and ‘indirect’, through changes in healthcare. Methods and results We used (i) national mortality data for England and Wales to investigate trends in non-COVID-19 and CVD excess deaths; (ii) routine data from hospitals in England (n = 2), Italy (n = 1), and China (n = 5) to assess indirect pandemic effects on referral, diagnosis, and treatment services for CVD; and (iii) population-based electronic health records from 3 862 012 individuals in England to investigate pre- and post-COVID-19 mortality for people with incident and prevalent CVD. We incorporated pre-COVID-19 risk (by age, sex, and comorbidities), estimated population COVID-19 prevalence, and estimated relative risk (RR) of mortality in those with CVD and COVID-19 compared with CVD and non-infected (RR: 1.2, 1.5, 2.0, and 3.0). Mortality data suggest indirect effects on CVD will be delayed rather than contemporaneous (peak RR 1.14). CVD service activity decreased by 60–100% compared with pre-pandemic levels in eight hospitals across China, Italy, and England. In China, activity remained below pre-COVID-19 levels for 2–3 months even after easing lockdown and is still reduced in Italy and England. For total CVD (incident and prevalent), at 10% COVID-19 prevalence, we estimated direct impact of 31 205 and 62 410 excess deaths in England (RR 1.5 and 2.0, respectively), and indirect effect of 49 932 to 99 865 deaths. Conclusion Supply and demand for CVD services have dramatically reduced across countries with potential for substantial, but avoidable, excess mortality during and after the pandemic., Graphical Abstract Graphical Abstract

Patients and public have sought mortality risk information throughout the pandemic, but their needs may not be served by current risk prediction tools. Our mixed methods study involved: (1) systematic review of published risk tools for prognosis, (2) provision and patient testing of new mortality risk estimates for people with high-risk conditions and (3) iterative patient and public involvement and engagement with qualitative analysis. Only one of 53 (2%) previously published risk tools involved patients or the public, while 11/53 (21%) had publicly accessible portals, but all for use by clinicians and researchers. Among people with a wide range of underlying conditions, there has been sustained interest and engagement in accessible and tailored, pre- and postpandemic mortality information. Informed by patient feedback, we provide such information in ‘five clicks’ (https://covid19-phenomics.org/OurRiskCoV.html), as context for decision making and discussions with health professionals and family members. Further development requires curation and regular updating of NHS data and wider patient and public engagement.

Background Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. Methods In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. Findings Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. Interpretation Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. Funding British Heart Foundation Data Science Centre, led by Health Data Research UK.

BackgroundUpdatable understanding of the onset and progression of individuals COVID-19 trajectories underpins pandemic mitigation efforts. In order to identify and characterize individual trajectories, we defined and validated ten COVID-19 phenotypes from linked electronic health records (EHR) on a nationwide scale using an extensible framework.MethodsCohort study of 56.6 million people in England alive on 23/01/2020, followed until 31/05/2021, using eight linked national datasets spanning COVID-19 testing, vaccination, primary & secondary care and death registrations data. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity using a combination of international clinical terminologies (e.g. SNOMED-CT, ICD-10) and bespoke data fields; positive test, primary care diagnosis, hospitalisation, critical care (four phenotypes), and death (three phenotypes). Using these phenotypes, we constructed patient trajectories illustrating the transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status.FindingsWe identified 3,469,528 infected individuals (6.1%) with 8,825,738 recorded COVID-19 phenotypes. Of these, 364,260 (11%) were hospitalised and 140,908 (4%) died. Of those hospitalised, 38,072 (10%) were admitted to intensive care (ICU), 54,026 (15%) received non-invasive ventilation and 21,404 (6%) invasive ventilation. Amongst hospitalised patients, first wave mortality (30%) was higher than the second (23%) in non-ICU settings, but remained unchanged for ICU patients. The highest mortality was for patients receiving critical care outside of ICU in wave 1 (51%). 13,083 (9%) COVID-19 related deaths occurred without diagnoses on the death certificate, but within 30 days of a positive test while 10,403 (7%) of cases were identified from mortality data alone with no prior phenotypes recorded. We observed longer patient trajectories in the second pandemic wave compared to the first.InterpretationOur analyses illustrate the wide spectrum of severity that COVID-19 displays and significant differences in incidence, survival and pathways across pandemic waves. We provide an adaptable framework to answer questions of clinical and policy relevance; new variant impact, booster dose efficacy and a way of maximising existing data to understand individuals progression through disease states.Research in ContextEvidence before the studyWe searched PubMed on October 14, 2021, for publications with the terms “COVID-19” or “SARS-CoV-2”, “severity”, and “electronic health records” or “EHR” without date or language restrictions. Multiple studies explore factors associated with severity of COVID-19 infection, and model predictions of outcome for hospitalised patients. However, most work to date focused on isolated facets of the healthcare system, such as primary or secondary care only, was conducted in subpopulations (e.g. hospitalised patients) of limited sample size, and often utilized dichotomised outcomes (e.g. mortality or hospitalisation) ignoring the full spectrum of disease. We identified no studies which comprehensively detailed severity of infections while describing disease severity across pandemic waves, vaccination status, and patient trajectories.Added value of this studyTo our knowledge, this is the first study providing a comprehensive view of COVID-19 across pandemic waves using national data and focusing on severity, vaccination, and patient trajectories. Drawing on linked electronic health record (EHR) data on a national scale (56.6 million people alive and registered with GP in England), we describe key demographic factors, frequency of comorbidities, impact of the two main waves in England, and effect of full vaccination on COVID-19 severities. Additionally, we identify and describe patient trajectory networks which illustrate the main transition pathways of COVID-19 patients in the healthcare system. Finally, we provide reproducible COVID-19 phenotyping algorithms reflecting clinically relevant stages of disease severity i.e. positive tests, primary care diagnoses, hospitalisation, critical care treatments (e.g. ventilatory support) and mortality.Implications of all the available evidenceThe COVID-19 phenotypes and trajectory analysis framework outlined produce a reproducible, extensible and repurposable means to generate national-scale data to support critical policy decision making. By modelling patient trajectories as a series of interactions with healthcare systems, and linking these to demographic and outcome data, we provide a means to identify and prioritise care pathways associated with adverse outcomes and highlight healthcare system ‘touch points’ which may act as tangible targets for intervention.

Background An Informatics Consult has been proposed in which clinicians request novel evidence from large scale health data resources, tailored to the treatment of a specific patient. However, the availability of such consultations is lacking. We seek to provide an Informatics Consult for a situation where a treatment indication and contraindication coexist in the same patient, i.e., anti-coagulation use for stroke prevention in a patient with both atrial fibrillation (AF) and liver cirrhosis. Methods We examined four sources of evidence for the effect of warfarin on stroke risk or all-cause mortality from: (1) randomised controlled trials (RCTs), (2) meta-analysis of prior observational studies, (3) trial emulation (using population electronic health records (N = 3,854,710) and (4) genetic evidence (Mendelian randomisation). We developed prototype forms to request an Informatics Consult and return of results in electronic health record systems. Results We found 0 RCT reports and 0 trials recruiting for patients with AF and cirrhosis. We found broad concordance across the three new sources of evidence we generated. Meta-analysis of prior observational studies showed that warfarin use was associated with lower stroke risk (hazard ratio [HR] = 0.71, CI 0.39–1.29). In a target trial emulation, warfarin was associated with lower all-cause mortality (HR = 0.61, CI 0.49–0.76) and ischaemic stroke (HR = 0.27, CI 0.08–0.91). Mendelian randomisation served as a drug target validation where we found that lower levels of vitamin K1 (warfarin is a vitamin K1 antagonist) are associated with lower stroke risk. A pilot survey with an independent sample of 34 clinicians revealed that 85% of clinicians found information on prognosis useful and that 79% thought that they should have access to the Informatics Consult as a service within their healthcare systems. We identified candidate steps for automation to scale evidence generation and to accelerate the return of results. Conclusion We performed a proof-of-concept Informatics Consult for evidence generation, which may inform treatment decisions in situations where there is dearth of randomised trials. Patients are surprised to know that their clinicians are currently not able to learn in clinic from data on ‘patients like me’. We identify the key challenges in offering such an Informatics Consult as a service.

Background People with liver disease are at increased risk of developing cardiovascular disease (CVD), however, there has yet been an investigation of incidence burden, risk, and premature mortality across a wide range of liver conditions and cardiovascular outcomes. Methods We employed population-wide electronic health records (EHRs; from 1998 to 2020) consisting of almost 4 million adults to assess regional variations in disease burden of five liver conditions, alcoholic liver disease (ALD), autoimmune liver disease, chronic hepatitis B infection (HBV), chronic hepatitis C infection (HCV) and NAFLD, in England. We analysed regional differences in incidence rates for 17 manifestations of CVD in people with or without liver disease. The associations between biomarkers and comorbidities and risk of CVD in patients with liver disease were estimated using Cox models. For each liver condition, we estimated excess years of life lost (YLL) attributable to CVD (i.e., difference in YLL between people with or without CVD). Results The age-standardised incidence rate for any liver disease was 114.5 per 100,000 person years. The highest incidence was observed in NAFLD (85.5), followed by ALD (24.7), HCV (6.0), HBV (4.1) and autoimmune liver disease (3.7). Regionally, the North West and North East regions consistently exhibited high incidence burden. Age-specific incidence rate analyses revealed that the peak incidence for liver disease of non-viral aetiology is reached in individuals aged 50–59 years. Patients with liver disease had a two-fold higher incidence burden of CVD (2634.6 per 100,000 persons) compared to individuals without liver disease (1339.7 per 100,000 persons). When comparing across liver diseases, atrial fibrillation was the most common initial CVD presentation while hypertrophic cardiomyopathy was the least common. We noted strong positive associations between body mass index and current smoking and risk of CVD. Patients who also had diabetes, hypertension, proteinuric kidney disease, chronic kidney disease, diverticular disease and gastro-oesophageal reflex disorders had a higher risk of CVD, as do patients with low albumin, raised C-reactive protein and raised International Normalized Ratio levels. All types of CVD were associated with shorter life expectancies. When evaluating excess YLLs by age of CVD onset and by liver disease type, differences in YLLs, when comparing across CVD types, were more pronounced at younger ages. Conclusions We developed a public online app (https://lailab.shinyapps.io/cvd_in_liver_disease/) to showcase results interactively. We provide a blueprint that revealed previously underappreciated clinical factors related to the risk of CVD, which differed in the magnitude of effects across liver diseases. We found significant geographical variations in the burden of liver disease and CVD, highlighting the need to devise local solutions. Targeted policies and regional initiatives addressing underserved communities might help improve equity of access to CVD screening and treatment.

BackgroundCancer is a life-altering event causing considerable psychological distress. However, population-representative variations in the total burden of psychiatric episodes across cancer types and treatment modalities have not been examined. We sought to estimate the risk of self-harm after incident psychiatric disorder diagnosis in patients with cancer, and the risk of unnatural deaths after self-harm.Design, Setting, ParticipantsPopulation-based cohort study with multiphase study designs. Population-based linked patient records in England (1998-2020) from primary care practices, hospitals, cancer registry and death registry were employed. We identified 459,542 individuals age ≥ 18 years with an incident diagnosis of a site-specific cancer of interest.Main outcome measuresUsing outpatient and inpatient records, we identified patients with five psychiatric disorders of interest: depression, anxiety disorder, schizophrenia, bipolar disorder and personality disorder. Cumulative burden for all psychiatric events was estimated using the mean cumulative count method. We considered 10 cancer treatment regimens, 11 chemotherapy drug classes, deprivation status and 21 non-cancer comorbidities in stratified analyses. Propensity score matching was employed to identify controls who did not have any record of a psychiatric disorder of interest. For each psychiatric disorder category, we fitted a Cox regression model to estimate the risk of self-harm. We also estimated the risk of all-cause mortality and excess years of life lost comparing patients with and without psychiatric disorders. A separate matched cohort was generated to explore the risk of suicide and unnatural deaths following self-harm.ResultsDepression was the most common psychiatric disorder in patients with cancer, where some of the highest cumulative burdens were observed in patients with testicular cancer (98.05 per 100 individuals [CI: 83.08-127.25]), cervical cancer (78.74 [73.61-90.14]) and Hodgkin lymphoma (69.87 [61.05-69.48]) by age 60. Patients who received chemotherapy, radiotherapy and surgery had the highest cumulative burden of psychiatric disorders, while patients who received radiotherapy alone had the lowest burden. Patients treated with alkylating agent chemotherapeutics had the highest burden of psychiatric disorders while those treated with kinase inhibitors had the lowest burden. Among patients with cancer, 5,683 individuals were identified as having an incident self-harm episode. A previous diagnosis of psychiatric disorder before self-harm was at least twice as prevalent than a subsequent diagnosis of psychiatric disorder where the prevalence ratio was the highest in patients with brain tumours (5.36, CI: 4.57-6.14). Younger individuals were more likely to be diagnosed with mental illness before the first self-harm episode. However, individuals from more deprived regions (2.46, CI: 2.32-2.60) and individuals with ≥4 pre-existing comorbidities (2.19, CI: 1.92-2.46) were less likely to be diagnosed with mental illness before self-harm. Patients with mental illness had a higher cumulative burden of self-harm events compared with matched controls. All mental illnesses were associated with an increased risk of subsequent self-harm, where the highest risk was observed within 12 months of the mental illness diagnosis. Risks of self-harm during the first year in matched cohorts were as follow: depression (adjusted HR 44.1, CI: 34.0-57.1), anxiety disorder (HR 21.1, CI: 16.4-27.0) and schizophrenia (HR 7.5, CI: 5.0-11.2). Patients with cancer and psychiatric disorder experienced excess years of life lost. Patients who harmed themselves were 6.8 times more likely to die of unnatural causes of death compared with controls within 12 months of self-harm (HR 6.8, CI: 4.3-10.7). The risk of unnatural death after 12 months was markedly lower (HR 2.0, CI: 1.5-2.7).ConclusionsThis study quantifies the total burden of psychiatric events and self-harm in patients with cancer. The cumulative burden of psychiatric events varies across cancer type, treatment regimen and chemotherapy type. Incident psychiatric disorder diagnoses were significantly associated with increased risk of subsequent self-harm, where risks varied across psychiatric diagnostic categories and follow-up periods. Patients who harm themselves experienced the highest risk of dying from unnatural deaths within the first year of self-harm. We provide an extensive knowledge base to help inform collaborative cancer-psychiatric care initiatives by prioritising patients who are most at risk.

Funder: Department of Health, Funder: Medical Research Council, BackgroundTargeted obesity prevention policies would benefit from the identification of population groups with the highest risk of weight gain. The relative importance of adult age, sex, ethnicity, geographical region, and degree of social deprivation on weight gain is not known. We aimed to identify high-risk groups for changes in weight and BMI using electronic health records (EHR).MethodsIn this longitudinal, population-based cohort study we used linked EHR data from 400 primary care practices (via the Clinical Practice Research Datalink) in England, accessed via the CALIBER programme. Eligible participants were aged 18-74 years, were registered at a general practice clinic, and had BMI and weight measurements recorded between Jan 1, 1998, and June 30, 2016, during the period when they had eligible linked data with at least 1 year of follow-up time. We calculated longitudinal changes in BMI over 1, 5, and 10 years, and investigated the absolute risk and odds ratios (ORs) of transitioning between BMI categories (underweight, normal weight, overweight, obesity class 1 and 2, and severe obesity [class 3]), as defined by WHO. The associations of demographic factors with BMI transitions were estimated by use of logistic regression analysis, adjusting for baseline BMI, family history of cardiovascular disease, use of diuretics, and prevalent chronic conditions.FindingsWe included 2 092 260 eligible individuals with more than 9 million BMI measurements in our study. Young adult age was the strongest risk factor for weight gain at 1, 5, and 10 years of follow-up. Compared with the oldest age group (65-74 years), adults in the youngest age group (18-24 years) had the highest OR (4·22 [95% CI 3·86-4·62]) and greatest absolute risk (37% vs 24%) of transitioning from normal weight to overweight or obesity at 10 years. Likewise, adults in the youngest age group with overweight or obesity at baseline were also at highest risk to transition to a higher BMI category; OR 4·60 (4·06-5·22) and absolute risk (42% vs 18%) of transitioning from overweight to class 1 and 2 obesity, and OR 5·87 (5·23-6·59) and absolute risk (22% vs 5%) of transitioning from class 1 and 2 obesity to class 3 obesity. Other demographic factors were consistently less strongly associated with these transitions; for example, the OR of transitioning from normal weight to overweight or obesity in people living in the most socially deprived versus least deprived areas was 1·23 (1·18-1·27), for men versus women was 1·12 (1·08-1·16), and for Black individuals versus White individuals was 1·13 (1·04-1·24). We provide an open access online risk calculator, and present high-resolution obesity risk charts over a 1-year, 5-year, and 10-year follow-up period.InterpretationA radical shift in policy is required to focus on individuals at the highest risk of weight gain (ie, young adults aged 18-24 years) for individual-level and population-level prevention of obesity and its long-term consequences for health and health care.FundingThe British Hearth Foundation, Health Data Research UK, the UK Medical Research Council, and the National Institute for Health Research.

Supplemental Digital Content is available in the text., Background: Cross-sectional measures of body mass index (BMI) are associated with cardiovascular disease (CVD) incidence, but less is known about whether weight change affects the risk of CVD. Methods: We estimated the effect of 2-y weight change interventions on 7-y risk of CVD (CVD death, myocardial infarction, stroke, hospitalization from coronary heart disease, and heart failure) by emulating hypothetical interventions using electronic health records. We identified 138,567 individuals with 45–69 years of age without chronic disease in England from 1998 to 2016. We performed pooled logistic regression, using inverse-probability weighting to adjust for baseline and time-varying confounders. We categorized each individual into a weight loss, maintenance, or gain group. Results: Among those of normal weight, both weight loss [risk difference (RD) vs. weight maintenance = 1.5% (0.3% to 3.0%)] and gain [RD = 1.3% (0.5% to 2.2%)] were associated with increased risk for CVD compared with weight maintenance. Among overweight individuals, we observed moderately higher risk of CVD in both the weight loss [RD = 0.7% (−0.2% to 1.7%)] and the weight gain group [RD = 0.7% (−0.1% to 1.7%)], compared with maintenance. In the obese, those losing weight showed lower risk of coronary heart disease [RD = −1.4% (−2.4% to −0.6%)] but not of stroke. When we assumed that chronic disease occurred 1–3 years before the recorded date, estimates for weight loss and gain were attenuated among overweight individuals; estimates for loss were lower among obese individuals. Conclusion: Among individuals with obesity, the weight-loss group had a lower risk of coronary heart disease but not of stroke. Weight gain was associated with increased risk of CVD across BMI groups. See video abstract at, http://links.lww.com/EDE/B838.

TALE class genes are a group of developmentally conserved transcription factors found in animals. Here, we describe the identification and phylogenetic analysis of TALE class genes in amphipod crustaceans. We identified 241 putative TALE class genes from 56 amphipod crustacean species. Phylogenetic analysis of the genes revealed six subclasses. We provide a list of FASTA sequences of the genes identified. Results from this work may inform future evolutionary and comparative genomics studies on animal development.

Overactive DNA repair contributes to therapeutic resistance in cancer. However, pan-cancer comparative studies investigating the contribution of all DNA repair genes in cancer progression employing an integrated approach have remained limited. We performed a multicohort retrospective analysis to determine the prognostic significance of 138 DNA repair genes in 16 cancer types (n=16,225). Cox proportional hazards analyses revealed a significant variation in the number of prognostic genes between cancers; 81 genes were prognostic in clear cell renal cell carcinoma while only two genes were prognostic in glioblastoma. We reasoned that genes that were commonly prognostic in highly correlated cancers revealed by Spearman’s correlation analysis could be harnessed as a molecular signature for risk assessment. A 10-gene signature, uniting prognostic genes that were common in highly correlated cancers, was significantly associated with overall survival in patients with clear cell renal cell (PTP53. The 10-gene signature identified tumors with heightened DNA repair ability. This information has the potential to radically change prognosis through the use of adjuvant DNA repair inhibitors with chemotherapeutic drugs.

ObjectivesDescribe a new England-wide electronic health record (EHR) resource enabling whole population research on Covid-19 and cardiovascular disease whilst ensuring data security and privacy and maintaining public trust.DesignCohort comprising linked person-level records from national healthcare settings for the English population accessible within NHS Digital’s new Trusted Research Environment.SettingEHRs from primary care, hospital episodes, death registry, Covid-19 laboratory test results and community dispensing data, with further enrichment planned from specialist intensive care, cardiovascular and Covid-19 vaccination data.Participants54.4 million people alive on 1st January 2020 and registered with an NHS general practitioner in England.Main measures of interestConfirmed and suspected Covid-19 diagnoses, exemplar cardiovascular conditions (incident stroke or transient ischaemic attack (TIA) and incident myocardial infarction (MI)) and all-cause mortality between 1st January and 31st October 2020.ResultsThe linked cohort includes over 96% of the English population. By combining person-level data across national healthcare settings, data on age, sex and ethnicity are complete for over 95% of the population. Among 53.2M people with no prior diagnosis of stroke/TIA, 98,721 had an incident stroke/TIA, of which 30% were recorded only in primary care and 4% only in death registry records. Among 53.1M people with no prior history of MI, 62,966 had an incident MI, of which 8% were recorded only in primary care and 12% only in death records. A total of 959,067 people had a confirmed or suspected Covid-19 diagnosis (714,162 in primary care data, 126,349 in hospital admission records, 776,503 in Covid-19 laboratory test data and 48,433 participants in death registry records). While 58% of these were recorded in both primary care and Covid-19 laboratory test data, 15% and 18% respectively were recorded in only one.ConclusionsThis population-wide resource demonstrates the importance of linking person-level data across health settings to maximize completeness of key characteristics and to ascertain cardiovascular events and Covid-19 diagnoses. Although established initially to support research on Covid-19 and cardiovascular disease to benefit clinical care and public health and to inform health care policy, it can broaden further to enable a very wide range of research.

BackgroundAdult obesity prevention policies, which are largely untargeted, have met with limited success globally. Population groups with the highest risk of weight gain, if they could be reliably identified using readily available information, might benefit from targeted policy. The relative importance of age, sex, ethnicity, geographical region and social deprivation for weight gain is unknown.MethodsWe calculated longitudinal changes in BMI over one, five and ten years and investigated transition between BMI categories using 11,187,383 clinically recorded, repeated measures of BMI from population-based electronic health records of 2,328,477 adults in England (1998-2016). The influence of risk factors was tested using logistic regression.FindingsThe youngest adult age group (18-24 years) was more strongly associated with risk of weight gain than older age, male sex, socioeconomic deprivation, ethnicity or geographic region. Among the youngest adults, the top quartile gained 15.9kg in men and 12kg in women at 10 years. The odds of transitioning to a higher BMI category over 10 years were 4-6 times higher in the youngest (18-24 years) compared to oldest (65-74 years) individuals; odds ratio (95% confidence interval) 4.22 (3.85-4.62)) from normal weight to overweight or obesity, 4.60 (4.06-5.22) for overweight to obesity, and 5.87 (5.23-6.59) from obesity to severe obesity in multiple adjusted analyses. Among the youngest adults, socially deprived men were at greater risk of transitioning from normal weight to overweight (72%) and from overweight to obesity (68%) over 10 years. We provide an open access online risk calculator (https://pasea.shinyapps.io/bmi_shiny_app/) and present high resolution obesity risk charts over a 1-, 5- and 10-year follow-up.InterpretationA radical shift in policy is required to focus on those at highest risk of weight gain-young adults-for individual and population level prevention of obesity and its long-term consequences for health and health care.FundingBHF, HDR-UK, MRC

ObjectivesTo estimate the impact of the COVID-19 pandemic on cancer care services and overall (direct and indirect) excess deaths in people with cancer.MethodsWe employed near real-time weekly data on cancer care to determine the adverse effect of the pandemic on cancer services. We also used these data, together with national death registrations until June 2020 to model deaths, in excess of background (pre-COVID-19) mortality, in people with cancer. Background mortality risks for 24 cancers with and without COVID-19-relevant comorbidities were obtained from population-based primary care cohort (Clinical Practice Research Datalink) on 3 862 012 adults in England.ResultsDeclines in urgent referrals (median=−70.4%) and chemotherapy attendances (median=−41.5%) to a nadir (lowest point) in the pandemic were observed. By 31 May, these declines have only partially recovered; urgent referrals (median=−44.5%) and chemotherapy attendances (median=−31.2%). There were short-term excess death registrations for cancer (without COVID-19), with peak relative risk (RR) of 1.17 at week ending on 3 April. The peak RR for all-cause deaths was 2.1 from week ending on 17 April. Based on these findings and recent literature, we modelled 40% and 80% of cancer patients being affected by the pandemic in the long-term. At 40% affected, we estimated 1-year total (direct and indirect) excess deaths in people with cancer as between 7165 and 17 910, using RRs of 1.2 and 1.5, respectively, where 78% of excess deaths occured in patients with ≥1 comorbidity.ConclusionsDramatic reductions were detected in the demand for, and supply of, cancer services which have not fully recovered with lockdown easing. These may contribute, over a 1-year time horizon, to substantial excess mortality among people with cancer and multimorbidity. It is urgent to understand how the recovery of general practitioner, oncology and other hospital services might best mitigate these long-term excess mortality risks.