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1.
BMJ Open ; 13(1), 2023.
Article in English | ProQuest Central | ID: covidwho-2213958

ABSTRACT

ObjectivesThe COVID-19 pandemic has posed unprecedented challenges to health systems and populations, particularly in India. Comprehensive, population-level studies of the burden of disease could inform planning, preparedness and policy, but are lacking in India. In West Bengal, India, we conducted a detailed analysis of the burden caused by COVID-19 from its onset to 7 January 2022.SettingOpen-access, population-level and administrative data sets for West Bengal were used.Primary and secondary outcome measuresDisability-adjusted life years (DALYs), years of potential productive life lost (YPPLL), cost of productivity lost (CPL: premature mortality and absenteeism), years of potential life lost (YPLL), premature years of potential life lost, working years of potential life lost (WYPLL) and value of statistical life (VSL) were estimated across scenarios (21 for DALY and 3 each for YPLL and VSL) to evaluate the effects of different factors.ResultsCOVID-19 had a higher impact on the elderly population with 90.2% of deaths arising from people aged above 45. In males and females, respectively, DALYs were 190 568.1 and 117 310.0 years, YPPLL of the productive population was 28 714.7 and 16 355.4 years, CPL due to premature mortality was INR3 198 259 615.6 and INR583 397 335.1 and CPL due to morbidity was INR2 505 568 048.4 and INR763 720 886.1. For males and females, YPLL ranged from 189 103.2 to 272 787.5 years and 117 925.5 to 169 712.0 years for lower to higher age limits, and WYPLL was 54 333.9 and 30 942.2 years. VSL (INR million) for the lower, midpoint and upper life expectancies was 883 330.8;882 936.4;and 880 631.3, respectively. Vaccination was associated with reduced mortality.ConclusionsThe losses incurred due to COVID-19 in terms of the computed estimates in West Bengal revealed a disproportionately higher impact on the elderly and males. Analysis of various age-gender subgroups enhances localised and targeted policymaking to minimise the losses for future pandemics.

2.
Glob Heart ; 17(1): 40, 2022.
Article in English | MEDLINE | ID: covidwho-2217353

ABSTRACT

Background and aims: Limited data exist on the cardiovascular manifestations and risk factors in people hospitalized with COVID-19 from low- and middle-income countries. This study aims to describe cardiovascular risk factors, clinical manifestations, and outcomes among patients hospitalized with COVID-19 in low, lower-middle, upper-middle- and high-income countries (LIC, LMIC, UMIC, HIC). Methods: Through a prospective cohort study, data on demographics and pre-existing conditions at hospital admission, clinical outcomes at hospital discharge (death, major adverse cardiovascular events (MACE), renal failure, neurological events, and pulmonary outcomes), 30-day vital status, and re-hospitalization were collected. Descriptive analyses and multivariable log-binomial regression models, adjusted for age, sex, ethnicity/income groups, and clinical characteristics, were performed. Results: Forty hospitals from 23 countries recruited 5,313 patients with COVID-19 (LIC = 7.1%, LMIC = 47.5%, UMIC = 19.6%, HIC = 25.7%). Mean age was 57.0 (±16.1) years, male 59.4%, pre-existing conditions included: hypertension 47.3%, diabetes 32.0%, coronary heart disease 10.9%, and heart failure 5.5%. The most frequently reported cardiovascular discharge diagnoses were cardiac arrest (5.5%), acute heart failure (3.8%), and myocardial infarction (1.6%). The rate of in-hospital deaths was 12.9% (N = 683), and post-discharge 30 days deaths was 2.6% (N = 118) (overall death rate 15.1%). The most common causes of death were respiratory failure (39.3%) and sudden cardiac death (20.0%). The predictors of overall mortality included older age (≥60 years), male sex, pre-existing coronary heart disease, renal disease, diabetes, ICU admission, oxygen therapy, and higher respiratory rates (p < 0.001 for each). Compared to Caucasians, Asians, Blacks, and Hispanics had almost 2-4 times higher risk of death. Further, patients from LIC, LMIC, UMIC versus. HIC had 2-3 times increased risk of death. Conclusions: The LIC, LMIC, and UMIC's have sparse data on COVID-19. We provide robust evidence on COVID-19 outcomes in these countries. This study can help guide future health care planning for the pandemic globally.


Subject(s)
COVID-19 , Cardiovascular Diseases , Diabetes Mellitus , Heart Failure , Aftercare , COVID-19/epidemiology , Cardiovascular Diseases/epidemiology , Heart Disease Risk Factors , Hospitalization , Humans , Male , Middle Aged , Patient Discharge , Prospective Studies , Risk Factors
3.
Clinics and Research in Hepatology and Gastroenterology ; : 102087, 2023.
Article in English | ScienceDirect | ID: covidwho-2177684

ABSTRACT

Introduction Oesophageal cancer is associated with poor health outcomes. Upper GI (UGI) endoscopy is the gold standard for diagnosis but is associated with patient discomfort and low yield for cancer. We used a machine learning approach to create a model which predicted oesophageal cancer based on questionnaire responses. Methods We used data from 2 separate prospective cross-sectional studies: the Saliva to Predict rIsk of disease using Transcriptomics and epigenetics (SPIT) study and predicting RIsk of diSease using detailed Questionnaires (RISQ) study. We recruited patients from National Health Service (NHS) suspected cancer pathways as well as patients with known cancer. We identified patient characteristics and questionnaire responses which were most associated with the development of oesophageal cancer. Using the SPIT dataset, we trained seven different machine learning models, selecting the best area under the receiver operator curve (AUC) to create our final model. We further applied a cost function to maximise cancer detection. We then independently validated the model using the RISQ dataset. Results 807 patients were included in model training and testing, split in a 70:30 ratio. 294 patients were included in model validation. The best model during training was regularised logistic regression using 17 features (median AUC: 0.81, interquartile range (IQR): 0.69-0.85). For testing and validation datasets, the model achieved an AUC of 0.71 (95% CI: 0.61-0.81) and 0.92 (95% CI: 0.88-0.96) respectively. At a set cut off, our model achieved a sensitivity of 97.6% and specificity of 59.1%. We additionally piloted the model in 12 patients with gastric cancer;9/12 (75%) of patients were correctly classified. Conclusions We have developed and validated a risk stratification tool using a questionnaire approach. This could aid prioritising patients at high risk of having oesophageal cancer for endoscopy. Our tool could help address endoscopic backlogs caused by the COVID-19 pandemic.

5.
BMJ open quality ; 11(4), 2022.
Article in English | EuropePMC | ID: covidwho-2147049

ABSTRACT

Background The quality of recording and documentation of deteriorating patient management by health professionals has been challenged during the COVID-19 pandemic. Non-adherence to escalation and documentation guidelines increases risk of serious adverse events. Electronic health record (EHR)-integrated dashboards are auditing tools of patients’ status and clinicians’ performance, but neither the views nor the performance of health professionals have been assessed, relating to management of deteriorating patients. Objective To develop and evaluate a real-time dashboard of deteriorating patients’ assessment, referral and therapy. Settings Five academic hospitals in the largest National Health Service (NHS) trust in the UK (Barts Health NHS Trust). Intervention The dashboard was developed from EHR data to investigate patients with National Early Warning Score (NEWS2)>5, assessment, and escalation of deteriorating patients. We adopted the Plan, Do, Study, Act model and Standards for Quality Improvement Reporting Excellence framework to evaluate the dashboard. Design Mixed methods: (1) virtual, face-to-face, interviews and (2) retrospective descriptive EHR data analysis. Results We interviewed three nurses (two quality and safety and one informatics specialists). Participants perceived the dashboard as a facilitator for auditing NEWS2 recording and escalation of care to improve practice;(2) there is a need for guiding clinicians and adjusting data sources and metrics to enhance the functionality and usability. Data analysis (2019–2022) showed: (1) NEWS2 recording has gradually improved (May 2021–April 2022) from 64% to 83%;(2) referral and assessment completion increased (n: 170–6800 and 23–540, respectively). Conclusion The dashboard is an effective real-time data-driven method for improving the quality of managing deteriorating patients. Integrating health systems, a wider analysis NEWS2 and escalation of care metrics, and clinicians’ learning digital solutions will enhance functionality and experience to boost its value. There is a need to examine the generalisability of the dashboard through further validation and quality improvement studies.

6.
eClinicalMedicine ; 55:101762, 2023.
Article in English | ScienceDirect | ID: covidwho-2130639

ABSTRACT

Summary Background The aim of this study was to systematically synthesise the global evidence on the prevalence of persistent symptoms in a general post COVID-19 population. Methods A systematic literature search was conducted using multiple electronic databases (MEDLINE and The Cochrane Library, Scopus, CINAHL, and medRxiv) until January 2022. Studies with at least 100 people with confirmed or self-reported COVID-19 symptoms at ≥28 days following infection onset were included. Patient-reported outcome measures and clinical investigations were both assessed. Results were analysed descriptively, and meta-analyses were conducted to derive prevalence estimates. This study was pre-registered (PROSPERO-ID: CRD42021238247). Findings 194 studies totalling 735,006 participants were included, with five studies conducted in those <18 years of age. Most studies were conducted in Europe (n = 106) or Asia (n = 49), and the time to follow-up ranged from ≥28 days to 387 days. 122 studies reported data on hospitalised patients, 18 on non-hospitalised, and 54 on hospitalised and non-hospitalised combined (mixed). On average, at least 45% of COVID-19 survivors, regardless of hospitalisation status, went on to experience at least one unresolved symptom (mean follow-up 126 days). Fatigue was frequently reported across hospitalised (28.4%;95% CI 24.7%–32.5%), non-hospitalised (34.8%;95% CI 17.6%–57.2%), and mixed (25.2%;95% CI 17.7%–34.6%) cohorts. Amongst the hospitalised cohort, abnormal CT patterns/x-rays were frequently reported (45.3%;95% CI 35.3%–55.7%), alongside ground glass opacification (41.1%;95% CI 25.7%–58.5%), and impaired diffusion capacity for carbon monoxide (31.7%;95% CI 25.8%–3.2%). Interpretation Our work shows that 45% of COVID-19 survivors, regardless of hospitalisation status, were experiencing a range of unresolved symptoms at ∼ 4 months. Current understanding is limited by heterogeneous study design, follow-up durations, and measurement methods. Definition of subtypes of Long Covid is unclear, subsequently hampering effective treatment/management strategies. Funding No funding.

7.
Exp Physiol ; 2022 Nov 22.
Article in English | MEDLINE | ID: covidwho-2137303

ABSTRACT

NEW FINDINGS: What is the topic of this review? The emerging condition of long COVID, its epidemiology, pathophysiological impacts on patients of different backgrounds, physiological mechanisms emerging as explanations of the condition, and treatment strategies being trialled. The review leads from a Physiological Society online conference on this topic. What advances does it highlight? Progress in understanding the pathophysiology and cellular mechanisms underlying Long COVID and potential therapeutic and management strategies. ABSTRACT: Long COVID, the prolonged illness and fatigue suffered by a small proportion of those infected with SARS-CoV-2, is placing an increasing burden on individuals and society. A Physiological Society virtual meeting in February 2022 brought clinicians and researchers together to discuss the current understanding of long COVID mechanisms, risk factors and recovery. This review highlights the themes arising from that meeting. It considers the nature of long COVID, exploring its links with other post-viral illnesses such as myalgic encephalomyelitis/chronic fatigue syndrome, and highlights how long COVID research can help us better support those suffering from all post-viral syndromes. Long COVID research started particularly swiftly in populations routinely monitoring their physical performance - namely the military and elite athletes. The review highlights how the high degree of diagnosis, intervention and monitoring of success in these active populations can suggest management strategies for the wider population. We then consider how a key component of performance monitoring in active populations, cardiopulmonary exercise training, has revealed long COVID-related changes in physiology - including alterations in peripheral muscle function, ventilatory inefficiency and autonomic dysfunction. The nature and impact of dysautonomia are further discussed in relation to postural orthostatic tachycardia syndrome, fatigue and treatment strategies that aim to combat sympathetic overactivation by stimulating the vagus nerve. We then interrogate the mechanisms that underlie long COVID symptoms, with a focus on impaired oxygen delivery due to micro-clotting and disruption of cellular energy metabolism, before considering treatment strategies that indirectly or directly tackle these mechanisms. These include remote inspiratory muscle training and integrated care pathways that combine rehabilitation and drug interventions with research into long COVID healthcare access across different populations. Overall, this review showcases how physiological research reveals the changes that occur in long COVID and how different therapeutic strategies are being developed and tested to combat this condition.

8.
PLoS One ; 17(11): e0277936, 2022.
Article in English | MEDLINE | ID: covidwho-2140676

ABSTRACT

INTRODUCTION: As mortality rates from COVID-19 disease fall, the high prevalence of long-term sequelae (Long COVID) is becoming increasingly widespread, challenging healthcare systems globally. Traditional pathways of care for Long Term Conditions (LTCs) have tended to be managed by disease-specific specialties, an approach that has been ineffective in delivering care for patients with multi-morbidity. The multi-system nature of Long COVID and its impact on physical and psychological health demands a more effective model of holistic, integrated care. The evolution of integrated care systems (ICSs) in the UK presents an important opportunity to explore areas of mutual benefit to LTC, multi-morbidity and Long COVID care. There may be benefits in comparing and contrasting ICPs for Long COVID with ICPs for other LTCs. METHODS AND ANALYSIS: This study aims to evaluate health services requirements for ICPs for Long COVID and their applicability to other LTCs including multi-morbidity and the overlap with medically not yet explained symptoms (MNYES). The study will follow a Delphi design and involve an expert panel of stakeholders including people with lived experience, as well as clinicians with expertise in Long COVID and other LTCs. Study processes will include expert panel and moderator panel meetings, surveys, and interviews. The Delphi process is part of the overall STIMULATE-ICP programme, aimed at improving integrated care for people with Long COVID. ETHICS AND DISSEMINATION: Ethical approval for this Delphi study has been obtained (Research Governance Board of the University of York) as have approvals for the other STIMULATE-ICP studies. Study outcomes are likely to inform policy for ICPs across LTCs. Results will be disseminated through scientific publication, conference presentation and communications with patients and stakeholders involved in care of other LTCs and Long COVID. REGISTRATION: Researchregistry: https://www.researchregistry.com/browse-the-registry#home/registrationdetails/6246bfeeeaaed6001f08dadc/.

9.
J R Soc Med ; : 1410768221131897, 2022 Nov 14.
Article in English | MEDLINE | ID: covidwho-2117355

ABSTRACT

OBJECTIVES: To use national, pre- and post-pandemic electronic health records (EHR) to develop and validate a scenario-based model incorporating baseline mortality risk, infection rate (IR) and relative risk (RR) of death for prediction of excess deaths. DESIGN: An EHR-based, retrospective cohort study. SETTING: Linked EHR in Clinical Practice Research Datalink (CPRD); and linked EHR and COVID-19 data in England provided in NHS Digital Trusted Research Environment (TRE). PARTICIPANTS: In the development (CPRD) and validation (TRE) cohorts, we included 3.8 million and 35.1 million individuals aged ≥30 years, respectively. MAIN OUTCOME MEASURES: One-year all-cause excess deaths related to COVID-19 from March 2020 to March 2021. RESULTS: From 1 March 2020 to 1 March 2021, there were 127,020 observed excess deaths. Observed RR was 4.34% (95% CI, 4.31-4.38) and IR was 6.27% (95% CI, 6.26-6.28). In the validation cohort, predicted one-year excess deaths were 100,338 compared with the observed 127,020 deaths with a ratio of predicted to observed excess deaths of 0.79. CONCLUSIONS: We show that a simple, parsimonious model incorporating baseline mortality risk, one-year IR and RR of the pandemic can be used for scenario-based prediction of excess deaths in the early stages of a pandemic. Our analyses show that EHR could inform pandemic planning and surveillance, despite limited use in emergency preparedness to date. Although infection dynamics are important in the prediction of mortality, future models should take greater account of underlying conditions.

10.
Eur Heart J Qual Care Clin Outcomes ; 2022 Nov 16.
Article in English | MEDLINE | ID: covidwho-2118038

ABSTRACT

BACKGROUND: Although morbidity and mortality from COVID-19 have been widely reported, the indirect effects of the pandemic beyond 2020 on other major diseases and health service activity have not been well described. METHODS: Analyses used national administrative electronic hospital records in England, Scotland and Wales for 2016-2021. Admissions and procedures during the pandemic (2020-2021) related to six major cardiovascular conditions (acute coronary syndrome, heart failure, stroke/transient ischaemic attack, peripheral arterial disease, aortic aneurysm, and venous thromboembolism) were compared to the annual average in the pre-pandemic period (2016-2019). Differences were assessed by time period and urgency of care. RESULTS: In 2020, there were 31 064 (-6%) fewer hospital admissions (14 506 [-4%] fewer emergencies, 16 560 [-23%] fewer elective admissions) compared to 2016-2019 for the six major cardiovascular diseases combined. The proportional reduction in admissions was similar in all three countries. Overall, hospital admissions returned to pre-pandemic levels in 2021. Elective admissions remained substantially below expected levels for almost all conditions in all three countries (-10 996 [-15%] fewer admissions). However, these reductions were offset by higher than expected total emergency admissions (+25 878 [+6%] higher admissions), notably for heart failure and stroke in England, and for venous thromboembolism in all three countries. Analyses for procedures showed similar temporal variations to admissions. CONCLUSION: This study highlights increasing emergency cardiovascular admissions during the pandemic, in the context of a substantial and sustained reduction in elective admissions and procedures. This is likely to increase further the demands on cardiovascular services over the coming years.

11.
Eur Heart J ; 43(37): 3578-3588, 2022 10 07.
Article in English | MEDLINE | ID: covidwho-2017894

ABSTRACT

Big data is central to new developments in global clinical science aiming to improve the lives of patients. Technological advances have led to the routine use of structured electronic healthcare records with the potential to address key gaps in clinical evidence. The covid-19 pandemic has demonstrated the potential of big data and related analytics, but also important pitfalls. Verification, validation, and data privacy, as well as the social mandate to undertake research are key challenges. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including patient representatives, clinicians, scientists, regulators, journal editors and industry. We propose the CODE-EHR Minimum Standards Framework as a means to improve the design of studies, enhance transparency and develop a roadmap towards more robust and effective utilisation of healthcare data for research purposes.


Subject(s)
COVID-19 , Electronic Health Records , COVID-19/epidemiology , Delivery of Health Care , Electronics , Humans , Pandemics/prevention & control
12.
Lancet Digit Health ; 4(10): e757-e764, 2022 10.
Article in English | MEDLINE | ID: covidwho-2004683

ABSTRACT

Big data is important to new developments in global clinical science that aim to improve the lives of patients. Technological advances have led to the regular use of structured electronic health-care records with the potential to address key deficits in clinical evidence that could improve patient care. The COVID-19 pandemic has shown this potential in big data and related analytics but has also revealed important limitations. Data verification, data validation, data privacy, and a mandate from the public to conduct research are important challenges to effective use of routine health-care data. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including representation from patients, clinicians, scientists, regulators, journal editors, and industry members. In this Review, we propose the CODE-EHR minimum standards framework to be used by researchers and clinicians to improve the design of studies and enhance transparency of study methods. The CODE-EHR framework aims to develop robust and effective utilisation of health-care data for research purposes.


Subject(s)
COVID-19 , Pandemics , Big Data , Electronic Health Records , Electronics , Humans
13.
JMIR Cardio ; 6(2): e37360, 2022 Aug 11.
Article in English | MEDLINE | ID: covidwho-1993690

ABSTRACT

BACKGROUND: Digital health interventions have become increasingly common across health care, both before and during the COVID-19 pandemic. Health inequalities, particularly with respect to ethnicity, may not be considered in frameworks that address the implementation of digital health interventions. We considered frameworks to include any models, theories, or taxonomies that describe or predict implementation, uptake, and use of digital health interventions. OBJECTIVE: We aimed to assess how health inequalities are addressed in frameworks relevant to the implementation, uptake, and use of digital health interventions; health and ethnic inequalities; and interventions for cardiometabolic disease. METHODS: SCOPUS, PubMed, EMBASE, Google Scholar, and gray literature were searched to identify papers on frameworks relevant to the implementation, uptake, and use of digital health interventions; ethnically or culturally diverse populations and health inequalities; and interventions for cardiometabolic disease. We assessed the extent to which frameworks address health inequalities, specifically ethnic inequalities; explored how they were addressed; and developed recommendations for good practice. RESULTS: Of 58 relevant papers, 22 (38%) included frameworks that referred to health inequalities. Inequalities were conceptualized as society-level, system-level, intervention-level, and individual. Only 5 frameworks considered all levels. Three frameworks considered how digital health interventions might interact with or exacerbate existing health inequalities, and 3 considered the process of health technology implementation, uptake, and use and suggested opportunities to improve equity in digital health. When ethnicity was considered, it was often within the broader concepts of social determinants of health. Only 3 frameworks explicitly addressed ethnicity: one focused on culturally tailoring digital health interventions, and 2 were applied to management of cardiometabolic disease. CONCLUSIONS: Existing frameworks evaluate implementation, uptake, and use of digital health interventions, but to consider factors related to ethnicity, it is necessary to look across frameworks. We have developed a visual guide of the key constructs across the 4 potential levels of action for digital health inequalities, which can be used to support future research and inform digital health policies.

14.
PLoS One ; 17(8): e0271978, 2022.
Article in English | MEDLINE | ID: covidwho-1993481

ABSTRACT

INTRODUCTION: Individuals with Long Covid represent a new and growing patient population. In England, fewer than 90 Long Covid clinics deliver assessment and treatment informed by NICE guidelines. However, a paucity of clinical trials or longitudinal cohort studies means that the epidemiology, clinical trajectory, healthcare utilisation and effectiveness of current Long Covid care are poorly documented, and that neither evidence-based treatments nor rehabilitation strategies exist. In addition, and in part due to pre-pandemic health inequalities, access to referral and care varies, and patient experience of the Long Covid care pathways can be poor. In a mixed methods study, we therefore aim to: (1) describe the usual healthcare, outcomes and resource utilisation of individuals with Long Covid; (2) assess the extent of inequalities in access to Long Covid care, and specifically to understand Long Covid patients' experiences of stigma and discrimination. METHODS AND ANALYSIS: A mixed methods study will address our aims. Qualitative data collection from patients and health professionals will be achieved through surveys, interviews and focus group discussions, to understand their experience and document the function of clinics. A patient cohort study will provide an understanding of outcomes and costs of care. Accessible data will be further analysed to understand the nature of Long Covid, and the care received. ETHICS AND DISSEMINATION: Ethical approval was obtained from South Central-Berkshire Research Ethics Committee (reference 303958). The dissemination plan will be decided by the patient and public involvement and engagement (PPIE) group members and study Co-Is, but will target 1) policy makers, and those responsible for commissioning and delivering Long Covid services, 2) patients and the public, and 3) academics.


Subject(s)
COVID-19 , Delivery of Health Care, Integrated , COVID-19/complications , COVID-19/epidemiology , COVID-19/therapy , Critical Pathways , Humans , Longitudinal Studies
16.
BMJ ; 378: e070695, 2022 08 02.
Article in English | MEDLINE | ID: covidwho-1968217

ABSTRACT

OBJECTIVE: To assess the risk of covid-19 death after infection with omicron BA.1 compared with delta (B.1.617.2). DESIGN: Retrospective cohort study. SETTING: England, United Kingdom, from 1 December 2021 to 30 December 2021. PARTICIPANTS: 1 035 149 people aged 18-100 years who tested positive for SARS-CoV-2 under the national surveillance programme and had an infection identified as omicron BA.1 or delta compatible. MAIN OUTCOME MEASURES: The main outcome measure was covid-19 death as identified from death certification records. The exposure of interest was the SARS-CoV-2 variant identified from NHS Test and Trace PCR positive tests taken in the community (pillar 2) and analysed by Lighthouse laboratories. Cause specific Cox proportional hazard regression models (censoring non-covid-19 deaths) were adjusted for sex, age, vaccination status, previous infection, calendar time, ethnicity, index of multiple deprivation rank, household deprivation, university degree, keyworker status, country of birth, main language, region, disability, and comorbidities. Interactions between variant and sex, age, vaccination status, and comorbidities were also investigated. RESULTS: The risk of covid-19 death was 66% lower (95% confidence interval 54% to 75%) for omicron BA.1 compared with delta after adjusting for a wide range of potential confounders. The reduction in the risk of covid-19 death for omicron compared with delta was more pronounced in people aged 18-59 years (number of deaths: delta=46, omicron=11; hazard ratio 0.14, 95% confidence interval 0.07 to 0.27) than in those aged ≥70 years (number of deaths: delta=113, omicron=135; hazard ratio 0.44, 95% confidence interval 0.32 to 0.61, P<0.0001). No evidence of a difference in risk was found between variant and number of comorbidities. CONCLUSIONS: The results support earlier studies showing a reduction in severity of infection with omicron BA.1 compared with delta in terms of hospital admission. This study extends the research to also show a reduction in the risk of covid-19 death for the omicron variant compared with the delta variant.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/mortality , COVID-19/virology , Humans , Proportional Hazards Models , Retrospective Studies , SARS-CoV-2/classification , SARS-CoV-2/pathogenicity
17.
Kidney Int ; 102(3): 652-660, 2022 09.
Article in English | MEDLINE | ID: covidwho-1945890

ABSTRACT

Chronic kidney disease (CKD) is associated with increased risk of baseline mortality and severe COVID-19, but analyses across CKD stages, and comorbidities are lacking. In prevalent and incident CKD, we investigated comorbidities, baseline risk, COVID-19 incidence, and predicted versus observed one-year excess death. In a national dataset (NHS Digital Trusted Research Environment [NHSD TRE]) for England encompassing 56 million individuals), we conducted a retrospective cohort study (March 2020 to March 2021) for prevalence of comorbidities by incident and prevalent CKD, SARS-CoV-2 infection and mortality. Baseline mortality risk, incidence and outcome of infection by comorbidities, controlling for age, sex and vaccination were assessed. Observed versus predicted one-year mortality at varying population infection rates and pandemic-related relative risks using our published model in pre-pandemic CKD cohorts (NHSD TRE and Clinical Practice Research Datalink [CPRD]) were compared. Among individuals with CKD (prevalent:1,934,585, incident:144,969), comorbidities were common (73.5% and 71.2% with one or more condition[s] in respective data sets, and 13.2% and 11.2% with three or more conditions, in prevalent and incident CKD), and associated with SARS-CoV-2 infection, particularly dialysis/transplantation (odds ratio 2.08, 95% confidence interval 2.04-2.13) and heart failure (1.73, 1.71-1.76), but not cancer (1.01, 1.01-1.04). One-year all-cause mortality varied by age, sex, multi-morbidity and CKD stage. Compared with 34,265 observed excess deaths, in the NHSD-TRE and CPRD databases respectively, we predicted 28,746 and 24,546 deaths (infection rates 10% and relative risks 3.0), and 23,754 and 20,283 deaths (observed infection rates 6.7% and relative risks 3.7). Thus, in this largest, national-level study, individuals with CKD have a high burden of comorbidities and multi-morbidity, and high risk of pre-pandemic and pandemic mortality. Hence, treatment of comorbidities, non-pharmaceutical measures, and vaccination are priorities for people with CKD and management of long-term conditions is important during and beyond the pandemic.


Subject(s)
COVID-19 , Renal Insufficiency, Chronic , COVID-19/epidemiology , COVID-19/therapy , Humans , Pandemics , Renal Insufficiency, Chronic/complications , Renal Insufficiency, Chronic/epidemiology , Renal Insufficiency, Chronic/therapy , Retrospective Studies , SARS-CoV-2
18.
Diabetes ; 71, 2022.
Article in English | ProQuest Central | ID: covidwho-1923965

ABSTRACT

Introduction: Both long COVID and Type 2 Diabetes (T2D) are multi-system conditions requiring multi-organ assessment to monitor organ health and detect co-morbidities earlier. Here, we defined multi-organ abnormalities in both patient groups with a rapid, non-contrast MRI scan. Methods: We recruited 135 long COVID patients (NCT04369807) and 135 T2D patients (NCT04114682) . MRI data were acquired for organ-specific measures of size, fat deposition and fibroinflammation (CoverScan®, Perspectum Ltd.) . Reference values were based on 92 controls and published literature. Results: There was a high prevalence of organ abnormality in both patient groups (Figure, left) , including increased fat deposition (steatosis) in liver, pancreas, and kidney (Figure, right) . 35% of T2D patients had clustering of abnormalities involving at least 2 organs, compared to 23% in long COVID. Abnormalities affecting the liver and renomegaly were more common in T2D than in long COVID. Considering only obese patients, liver fibroinflammation, hepatomegaly, and renomegaly remained significantly more prevalent in T2D than in long COVID. Conclusion: Multi-organ MRI assessment can enrich the current blunt assessment of multi-system abnormalities in diverse disease states to inform earlier intervention and treatments.

20.
Lancet Digit Health ; 4(7): e542-e557, 2022 07.
Article in English | MEDLINE | ID: covidwho-1882680

ABSTRACT

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.


Subject(s)
COVID-19 , COVID-19/epidemiology , COVID-19 Testing , Cohort Studies , Electronic Health Records , England/epidemiology , Humans , SARS-CoV-2 , State Medicine
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