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BACKGROUND: Long COVID describes new or persistent symptoms at least 4 weeks after onset of acute COVID-19. Clinical codes to describe this phenomenon were recently created. AIM: To describe the use of long-COVID codes, and variation of use by general practice, demographic variables, and over time. DESIGN AND SETTING: Population-based cohort study in English primary care. METHOD: Working on behalf of NHS England, OpenSAFELY data were used encompassing 96% of the English population between 1 February 2020 and 25 May 2021. The proportion of people with a recorded code for long COVID was measured overall and by demographic factors, electronic health record software system (EMIS or TPP), and week. RESULTS: Long COVID was recorded for 23 273 people. Coding was unevenly distributed among practices, with 26.7% of practices having never used the codes. Regional variation ranged between 20.3 per 100 000 people for East of England (95% confidence interval [CI] = 19.3 to 21.4) and 55.6 per 100 000 people in London (95% CI = 54.1 to 57.1). Coding was higher among females (52.1, 95% CI = 51.3 to 52.9) than males (28.1, 95% CI = 27.5 to 28.7), and higher among practices using EMIS (53.7, 95% CI = 52.9 to 54.4) than those using TPP (20.9, 95% CI = 20.3 to 21.4). CONCLUSION: Current recording of long COVID in primary care is very low, and variable between practices. This may reflect patients not presenting; clinicians and patients holding different diagnostic thresholds; or challenges with the design and communication of diagnostic codes. Increased awareness of diagnostic codes is recommended to facilitate research and planning of services, and also surveys with qualitative work to better evaluate clinicians' understanding of the diagnosis.

ObjectivesExcess mortality captures the total effect of the COVID-19 pandemic on mortality and is not affected by mis-specification of cause of death. We aimed to describe how health and demographic factors have been associated with excess mortality during the pandemic.DesignTime-series analysis.SettingUK primary care data from practices contributing to the Clinical Practice Research Datalink on July 31st 2020.ParticipantsWe constructed a time-series dataset including 9,635,613 adults (≥40 years old) who were actively registered at the general practice during the study period.Main outcome measuresWe extracted weekly numbers of deaths between March 2015 and July 2020, stratified by individual-level factors. Excess mortality during wave 1 of the UK pandemic (5th March to 27th May 2020) compared to pre-pandemic was estimated using seasonally adjusted negative binomial regression models. Relative rates of death for a range of factors were estimated before and during wave 1 by including interaction terms.ResultsAll-cause mortality increased by 43% (95% CI 40%-47%) during wave 1 compared with pre-pandemic. Changes to the relative rate of death associated with most socio-demographic and clinical characteristics were small during wave 1 compared with pre-pandemic. However, the mortality rate associated with dementia markedly increased (RR for dementia vs no dementia pre-pandemic: 3.5, 95% CI 3.4-3.5; RR during wave 1: 5.1, 4.87-5.28); a similar pattern was seen for learning disabilities (RR pre-pandemic: 3.6, 3.4-3.5; during wave 1: 4.8, 4.4-5.3), for Black or South Asian ethnicity compared to white, and for London compared to other regions.ConclusionsThe first UK COVID-19 wave appeared to amplify baseline mortality risk by a relatively constant factor for most population subgroups. However disproportionate increases in mortality were seen for those with dementia, learning disabilities, non-white ethnicity, or living in London.Summary boxWhat is already known on this topicAll-cause mortality during the COVID-19 pandemic was higher than in previous years; this excess mortality was particularly pronounced among elderly people, males, people of non-white ethnicity, people of lower socio-economic status and people living in care-homes.Several other papers have studied a wider range of factors associated with mortality due to COVID-19 using cause-of-death data.There is little evidence on how all-cause mortality has changed in people with comorbidities.What this study addsOur study shows that during Wave 1 of the pandemic all cause death rates increased by a similar proportional degree for almost all population subgroups regardless of their health or socio-demographic circumstances; the exceptions were those with a diagnosis of dementia or learning disabilities and those of non-white ethnicity or living in London.This suggests that COVID-19 has dialled up the risk of death by a similar proportional degree for everyone except those exposed to a higher risk of infection.

BackgroundPatients with COVID-19 are thought to be at higher risk of cardiometabolic and pulmonary complications, but quantification of that risk is limited. We aimed to describe the overall burden of these complications in survivors of severe COVID-19.MethodsWorking on behalf of NHS England, we used linked primary care records, death certificate and hospital data from the OpenSAFELY platform. We constructed three cohorts: patients discharged following hospitalisation with COVID-19, patients discharged following hospitalisation with pneumonia in 2019, and a frequency-matched cohort from the general population in 2019. We studied eight cardiometabolic and pulmonary outcomes. Absolute rates were measured in each cohort and Cox regression models were fitted to estimate age/sex adjusted hazard ratios comparing outcome rates between discharged COVID-19 patients and the two comparator cohorts.ResultsAmongst the population of 31,716 patients discharged following hospitalisation with COVID-19, rates for majority of outcomes peaked in the first month post-discharge, then declined over the following four months. Patients in the COVID-19 population had markedly increased risk of all outcomes compared to matched controls from the 2019 general population, especially for pulmonary embolism (HR 12.86; 95% CI: 11.23 - 14.74). Outcome rates were more similar when comparing patients discharged with COVID-19 to those discharged with pneumonia in 2019, although COVID-19 patients had increased risk of type 2 diabetes (HR 1.23; 95% CI: 1.05 - 1.44).InterpretationCardiometabolic and pulmonary adverse outcomes are markedly raised following hospitalisation for COVID-19 compared to the general population. However, the excess risks were more comparable to those seen following hospitalisation with pneumonia. Identifying patients at particularly high risk of outcomes would inform targeted preventive measures.FundingWellcome, Royal Society, National Institute for Health Research, National Institute for Health Research Oxford Biomedical Research Centre, UK Medical Research Council, UK Research and Innovation, Health and Safety Executive.

Background Targeted 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). Methods In 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. Findings We 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. Interpretation A 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. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.

Additional file 1. Additional figures and tables for main analyses and sensitivity analyses.

BackgroundClose contact with children may provide cross-reactive immunity to SARs-CoV-2 due to more frequent prior coryzal infections from seasonal coronaviruses. Alternatively, close contact with children may increase risk of SARs-CoV-2 infection. We investigated whether risk of infection with SARs-CoV-2 and severe outcomes differed between adults living with and without children.MethodsWorking on behalf of NHS England, we conducted a population-based cohort study using primary care data and pseudonymously-linked hospital and intensive care admissions, and death records, from patients registered in general practices representing 40% of England. Using multivariable Cox regression, we calculated fully-adjusted hazard ratios (HR) of outcomes from 1st February-3rd August 2020 comparing adults living with and without children in the household.FindingsAmong 9,157,814 adults ≤65 years, living with children 0-11 years was not associated with increased risks of recorded SARS-CoV-2 infection, COVID-19 related hospital or ICU admission but was associated with reduced risk of COVID-19 death (HR 0.75, 95%CI 0.62-0.92). Living with children aged 12-18 years was associated with a small increased risk of recorded SARS-CoV-2 infection (HR 1.08, 95%CI 1.03-1.13), but not associated with other COVID-19 outcomes. Living with children of any age was also associated with lower risk of dying from non-COVID-19 causes. Among 2,567,671 adults >65 years there was no association between living with children and outcomes related to SARS-CoV-2. We observed no consistent changes in risk following school closure.InterpretationFor adults living with children there is no evidence of an increased risk of severe COVID-19 outcomes. These findings have implications for determining the benefit-harm balance of children attending school in the COVID-19 pandemic.FundingThis work was supported by the Medical Research Council MR/V015737/1.Research in contextEvidence before this studyWe searched MEDLINE on 19th October 2020 for population-based epidemiological studies comparing the risk of SARS-CoV-2 infection and COVID-19 disease in people living with and without children. We searched for articles published in 2020, with abstracts available, and terms “(children or parents or dependants) AND (COVID or SARS-CoV-2 or coronavirus) AND (rate or hazard or odds or risk), in the title, abstract or keywords. 244 papers were identified for screening but none were relevant. One additional study in preprint was identified on medRxiv and found a reduced risk of hospitalisation for COVID-19 and a positive SARS-CoV-2 infection among adult healthcare workers living with children.Added value of this studyThis is the first population-based study to investigate whether the risk of recorded SARS-CoV-2 infection and severe outcomes from COVID-19 differ between adults living in households with and without school-aged children during the UK pandemic. Our findings show that for adults living with children there is no evidence of an increased risk of severe COVID-19 outcomes although there may be a slightly increased risk of recorded SARS-CoV-2 infection for working-age adults living with children aged 12 to 18 years. Working-age adults living with children 0 to 11 years have a lower risk of death from COVID-19 compared to adults living without children, with the effect size being comparable to their lower risk of death from any cause. We observed no consistent changes in risk of recorded SARS-CoV-2 infection and severe outcomes from COVID-19 comparing periods before and after school closure.Implications of all the available evidenceOur results demonstrate no evidence of serious harms from COVID-19 to adults in close contact with children, compared to those living in households without children. This has implications for determining the benefit-harm balance of children attending school in the COVID-19 pandemic.

IMPORTANCE: Associations between atopic eczema and cancer are unclear, with competing theories: that increased immune surveillance reduces cancer risk; and that immune stimulation increases risk. Establishing baseline cancer risk in people with atopic eczema is important prior to exploring the impact of new biologic drugs (for eczema) on cancer risk. OBJECTIVE: To investigate whether atopic eczema is associated with cancer. DESIGN: Matched cohort studies (England: 1998-2016; Denmark: 1982-2016). SETTING: English primary care, and nationwide Danish data. PARTICIPANTS: Individuals with atopic eczema (England: adults only; Denmark: any age) matched on age, sex, calendar period, and (in England only) primary care practice, to people without eczema. EXPOSURE: Atopic eczema MAIN OUTCOMES: We compared overall risk of cancer, and of 11 specific cancers, in people with and without eczema. RESULTS: We included 471,970 and 2,239,775 individuals with and without atopic eczema respectively in England; and 44,945 and 445,673 respectively in Denmark. We found little evidence of associations between atopic eczema and cancer overall (adjusted hazard ratios [HRs] [99%CI]: England 1.04 [1.02-1.06]; Denmark 1.05 [0.95-1.16]), or for most specific cancers. However, non-cutaneous lymphoma risk was raised in people with atopic eczema in England (adjusted HR [99%CI] 1.20 [1.07-1.34] for non-Hodgkin’s [NHL] and 1.48 [1.07-2.04] for Hodgkin’s). Lymphoma risk increased with greater eczema severity (NHL adjusted HR [99%CI] compared to without eczema: mild 1.06 [0.90-1.25], moderate 1.24 [1.04-1.48], severe 2.08 [1.42-3.04]). Danish point estimates also showed increased lymphoma in moderate-to-severe eczema compared to without (adjusted HR [99%CI]: NHL 1.31 [0.76-2.26]; Hodgkin’s 1.35 [0.65-2.28]), but confidence intervals were wide. CONCLUSIONS AND RELEVANCE: Our findings, from two large population-based studies in different settings, are largely reassuring as they do not support associations between eczema and most cancers. However, we observed an association between eczema and lymphoma that increased with eczema severity, which warrants further study with the introduction of new therapeutics that may impact cancer risk.

Routinely collected electronic health records (EHRs) are increasingly used for research. With their use comes the opportunity for large-scale, high-quality studies that can address questions not easily answered by randomised clinical trials or classical cohort studies involving bespoke data collection. However, the use of EHRs generates challenges in terms of ensuring methodological rigour, a potential problem when studying complex chronic diseases such as diabetes. This review describes the promises and potential of EHRs in the context of diabetes research and outlines key areas for caution with examples. We consider the difficulties in identifying and classifying diabetes patients, in distinguishing between prevalent and incident cases and in dealing with the complexities of diabetes progression and treatment. We also discuss the dangers of introducing time-related biases and describe the problems of inconsistent data recording, missing data and confounding. Throughout, we provide practical recommendations for good practice in conducting EHR studies and interpreting their results.