COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

Disparities by Social Determinants of Health: Links Between Long COVID and Cardiovascular Disease

Long COVID has been defined by the World Health Organisation as "continuation or development of new symptoms 3 months after the initial SARS-CoV-2 infection, with these symptoms lasting for at least 2 months with no other explanation." Cardiovascular disease is implicated as a risk factor, concomitant condition, and consequence of long COVID. As well as heterogeneity in definition, presentation, and likely underlying pathophysiology of long COVID, disparities by social determinants of health, extensively studied and described in cardiovascular disease, have been observed in 3 ways. First, underlying long-term conditions, such as cardiovascular disease and its risk factors, are associated with incidence and severity of long COVID, and previously described socioeconomic disparities in these factors are important in exacerbating disparities in long COVID. Second, socioeconomic disparities in management of COVID-19 may themselves lead to distal disparities in long COVID. Third, there are socioeconomic disparities in the way that long COVID is diagnosed, managed, and prevented. Together, factors such as age, sex, deprivation, and ethnicity have far-reaching implications in this new postviral syndrome across its management spectrum. There are similarities and differences compared with disparities for cardiovascular disease. Some of these disparities are in fact, inequalities, that is, rather than simply observed variations, they represent injustices with costs to individuals, communities, and economies. This review of current literature considers opportunities to prevent or at least attenuate these socioeconomic disparities in long COVID and cardiovascular disease, with special challenges for research, clinical practice, public health, and policy in a new disease which is evolving.

From test to rest: evaluating socioeconomic differences along the COVID-19 care pathway in the Netherlands

INTRODUCTION

The COVID-19 pandemic exacerbated healthcare needs and caused excess mortality, especially among lower socioeconomic groups. This study describes the emergence of socioeconomic differences along the COVID-19 pathway of testing, healthcare use and mortality in the Netherlands.

METHODOLOGY

This retrospective observational Dutch population-based study combined individual-level registry data from June 2020 to December 2020 on personal socioeconomic characteristics, COVID-19 administered tests, test results, general practitioner (GP) consultations, hospital admissions, Intensive Care Unit (ICU) admissions and mortality. For each outcome measure, relative differences between income groups were estimated using log-link binomial regression models. Furthermore, regression models explained socioeconomic differences in COVID-19 mortality by differences in ICU/hospital admissions, test administration and test results.

RESULTS

Among the Dutch population, the lowest income group had a lower test probability (RR = 0.61) and lower risk of testing positive (RR = 0.77) compared to the highest income group. However, among individuals with at least one administered COVID-19 test, the lowest income group had a higher risk of testing positive (RR = 1.40). The likelihood of hospital admissions and ICU admissions were higher for low income groups (RR = 2.11 and RR = 2.46, respectively). The lowest income group had an almost four times higher risk of dying from COVID-19 (RR = 3.85), which could partly be explained by a higher risk of hospitalization and ICU admission, rather than differences in test administration or result.

DISCUSSION

Our findings indicated that socioeconomic differences became more pronounced at each step of the care pathway, culminating to a large gap in mortality. This underlines the need for enhancing social security and well-being policies and incorporation of health equity in pandemic preparedness plans.

Observational methods for COVID-19 vaccine effectiveness research: an empirical evaluation and target trial emulation

BACKGROUND

There are scarce data on best practices to control for confounding in observational studies assessing vaccine effectiveness to prevent COVID-19. We compared the performance of three well-established methods [overlap weighting, inverse probability treatment weighting and propensity score (PS) matching] to minimize confounding when comparing vaccinated and unvaccinated people. Subsequently, we conducted a target trial emulation to study the ability of these methods to replicate COVID-19 vaccine trials.

METHODS

We included all individuals aged ≥75 from primary care records from the UK [Clinical Practice Research Datalink (CPRD) AURUM], who were not infected with or vaccinated against SARS-CoV-2 as of 4 January 2021. Vaccination status was then defined based on first COVID-19 vaccine dose exposure between 4 January 2021 and 28 January 2021. Lasso regression was used to calculate PS. Location, age, prior observation time, regional vaccination rates, testing effort and COVID-19 incidence rates at index date were forced into the PS. Following PS weighting and matching, the three methods were compared for remaining covariate imbalance and residual confounding. Last, a target trial emulation comparing COVID-19 at 3 and 12 weeks after first vaccine dose vs unvaccinated was conducted.

RESULTS

Vaccinated and unvaccinated cohorts comprised 583 813 and 332 315 individuals for weighting, respectively, and 459 000 individuals in the matched cohorts. Overlap weighting performed best in terms of minimizing confounding and systematic error. Overlap weighting successfully replicated estimates from clinical trials for vaccine effectiveness for ChAdOx1 (57%) and BNT162b2 (75%) at 12 weeks.

CONCLUSION

Overlap weighting performed best in our setting. Our results based on overlap weighting replicate previous pivotal trials for the two first COVID-19 vaccines approved in Europe.

Health outcomes after myocardial infarction: A population study of 56 million people in England

BACKGROUND

The occurrence of a range of health outcomes following myocardial infarction (MI) is unknown. Therefore, this study aimed to determine the long-term risk of major health outcomes following MI and generate sociodemographic stratified risk charts in order to inform care recommendations in the post-MI period and underpin shared decision making.

METHODS AND FINDINGS

This nationwide cohort study includes all individuals aged ≥18 years admitted to one of 229 National Health Service (NHS) Trusts in England between 1 January 2008 and 31 January 2017 (final follow-up 27 March 2017). We analysed 11 non-fatal health outcomes (subsequent MI and first hospitalisation for heart failure, atrial fibrillation, cerebrovascular disease, peripheral arterial disease, severe bleeding, renal failure, diabetes mellitus, dementia, depression, and cancer) and all-cause mortality. Of the 55,619,430 population of England, 34,116,257 individuals contributing to 145,912,852 hospitalisations were included (mean age 41.7 years (standard deviation [SD 26.1]); n = 14,747,198 (44.2%) male). There were 433,361 individuals with MI (mean age 67.4 years [SD 14.4)]; n = 283,742 (65.5%) male). Following MI, all-cause mortality was the most frequent event (adjusted cumulative incidence at 9 years 37.8% (95% confidence interval [CI] [37.6,37.9]), followed by heart failure (29.6%; 95% CI [29.4,29.7]), renal failure (27.2%; 95% CI [27.0,27.4]), atrial fibrillation (22.3%; 95% CI [22.2,22.5]), severe bleeding (19.0%; 95% CI [18.8,19.1]), diabetes (17.0%; 95% CI [16.9,17.1]), cancer (13.5%; 95% CI [13.3,13.6]), cerebrovascular disease (12.5%; 95% CI [12.4,12.7]), depression (8.9%; 95% CI [8.7,9.0]), dementia (7.8%; 95% CI [7.7,7.9]), subsequent MI (7.1%; 95% CI [7.0,7.2]), and peripheral arterial disease (6.5%; 95% CI [6.4,6.6]). Compared with a risk-set matched population of 2,001,310 individuals, first hospitalisation of all non-fatal health outcomes were increased after MI, except for dementia (adjusted hazard ratio [aHR] 1.01; 95% CI [0.99,1.02];p = 0.468) and cancer (aHR 0.56; 95% CI [0.56,0.57];p < 0.001). The study includes data from secondary care only-as such diagnoses made outside of secondary care may have been missed leading to the potential underestimation of the total burden of disease following MI.

CONCLUSIONS

In this study, up to a third of patients with MI developed heart failure or renal failure, 7% had another MI, and 38% died within 9 years (compared with 35% deaths among matched individuals). The incidence of all health outcomes, except dementia and cancer, was higher than expected during the normal life course without MI following adjustment for age, sex, year, and socioeconomic deprivation. Efforts targeted to prevent or limit the accrual of chronic, multisystem disease states following MI are needed and should be guided by the demographic-specific risk charts derived in this study.

Persons diagnosed with COVID-19 in England in the Clinical Practice Research Datalink (CPRD): a cohort description

OBJECTIVE

To create case definitions for confirmed COVID-19 diagnoses, COVID-19 vaccination status and three separate definitions of high risk of severe COVID-19, as well as to assess whether the implementation of these definitions in a cohort reflected the sociodemographic and clinical characteristics of COVID-19 epidemiology in England.

DESIGN

Retrospective cohort study.

SETTING

Electronic healthcare records from primary care (Clinical Practice Research Datalink, CPRD) linked to secondary care data (Hospital Episode Statistics) data covering 24% of the population in England.

PARTICIPANTS

2 271 072 persons aged 1 year and older diagnosed with COVID-19 in CPRD Aurum between 1 August 2020 and 31 January 2022.

MAIN OUTCOME MEASURES

Age, sex and regional distribution of COVID-19 cases and COVID-19 vaccine doses received prior to diagnosis were assessed separately for the cohorts of cases identified in primary care and those hospitalised for COVID-19 (primary diagnosis code of ICD-10 U07.1 'COVID-19'). Smoking status, body mass index and Charlson Comorbidity Index were compared for the two cohorts, as well as for three separate definitions of high risk of severe disease used in the UK (National Health Service Highest Risk, PANORAMIC trial eligibility, UK Health Security Agency Clinical Risk prioritisation for vaccination).

RESULTS

Compared with national estimates, CPRD case estimates under-represented older adults in both the primary care (age 65-84: 6% in CPRD vs 9% nationally) and hospitalised (31% vs 40%) cohorts, and over-represented people living in regions with the highest median wealth areas of England (20% primary care and 20% hospital admitted cases in South East vs 15% nationally). The majority of non-hospitalised cases and all hospitalised cases had not completed primary series vaccination. In primary care, persons meeting high-risk definitions were older, more often smokers, overweight or obese, and had higher Charlson Comorbidity Index score.

CONCLUSIONS

CPRD primary care data are a robust real-world data source and can be used for some COVID-19 research questions, however, limitations of the data availability should be carefully considered. Included in this publication are supplemental files for a total of over 28 000 codes to define each of three definitions of high risk of severe disease.

The future of cancer care in the UK-time for a radical and sustainable National Cancer Plan

Cancer affects one in two people in the UK and the incidence is set to increase. The UK National Health Service is facing major workforce deficits and cancer services have struggled to recover after the COVID-19 pandemic, with waiting times for cancer care becoming the worst on record. There are severe and widening disparities across the country and survival rates remain unacceptably poor for many cancers. This is at a time when cancer care has become increasingly complex, specialised, and expensive. The current crisis has deep historic roots, and to be reversed, the scale of the challenge must be acknowledged and a fundamental reset is required. The loss of a dedicated National Cancer Control Plan in England and Wales, poor operationalisation of plans elsewhere in the UK, and the closure of the National Cancer Research Institute have all added to a sense of strategic misdirection. The UK finds itself at a crossroads, where the political decisions of governments, the cancer community, and research funders will determine whether we can, together, achieve equitable, affordable, and high-quality cancer care for patients that is commensurate with our wealth, and position our outcomes among the best in the world. In this Policy Review, we describe the challenges and opportunities that are needed to develop radical, yet sustainable plans, which are comprehensive, evidence-based, integrated, patient-outcome focused, and deliver value for money.

Healthcare resource utilisation and costs of hospitalisation and primary care among adults with COVID-19 in England: a population-based cohort study

OBJECTIVES

To quantify direct costs and healthcare resource utilisation (HCRU) associated with acute COVID-19 in adults in England.

DESIGN

Population-based retrospective cohort study using Clinical Practice Research Datalink Aurum primary care electronic medical records linked to Hospital Episode Statistics secondary care administrative data.

SETTING

Patients registered to primary care practices in England.

POPULATION

1 706 368 adults with a positive SARS-CoV-2 PCR or antigen test from August 2020 to January 2022 were included; 13 105 within the hospitalised cohort indexed between August 2020 and March 2021, and 1 693 263 within the primary care cohort indexed between August 2020 and January 2022. Patients with a COVID-19-related hospitalisation within 84 days of a positive test were included in the hospitalised cohort.

MAIN OUTCOME MEASURES

Primary and secondary care HCRU and associated costs ≤4 weeks following positive COVID-19 test, stratified by age group, risk of severe COVID-19 and immunocompromised status.

RESULTS

Among the hospitalised cohort, average length of stay, including critical care stays, was longer in older adults. Median healthcare cost per hospitalisation was higher in those aged 75-84 (£8942) and ≥85 years (£8835) than in those aged <50 years (£7703). While few (6.0%) patients in critical care required mechanical ventilation, its use was higher in older adults (50-74 years: 8.3%; <50 years: 4.3%). HCRU and associated costs were often greater in those at higher risk of severe COVID-19 than in the overall cohort, although minimal differences in HCRU were found across the three different high-risk definitions. Among the primary care cohort, general practitioner or nurse consultations were more frequent among older adults and the immunocompromised.

CONCLUSIONS

COVID-19-related hospitalisations in older adults, particularly critical care stays, were the primary drivers of high COVID-19 resource use in England. These findings may inform health policy decisions and resource allocation in the prevention and management of COVID-19.

How is ethnicity reported, described, and analysed in health research in the UK? A bibliographical review and focus group discussions with young refugees

BACKGROUND

The ethnicity data gap pertains to 3 major challenges to address ethnic health inequality: 1) Under-representation of ethnic minorities in research; 2) Poor data quality on ethnicity; 3) Ethnicity data not being meaningfully analysed. These challenges are especially relevant for research involving under-served migrant populations in the UK. We aimed to review how ethnicity is captured, reported, analysed and theorised within policy-relevant research on ethnic health inequities.

METHODS

We reviewed a selection of the 1% most highly cited population health papers that reported UK data on ethnicity, and extracted how ethnicity was recorded and analysed in relation to health outcomes. We focused on how ethnicity was obtained (i.e. self reported or not), how ethnic groups were categorised, whether justification was provided for any categorisation, and how ethnicity was theorised to be related to health. We held three 1-h-long guided focus groups with 10 young people from Nigeria, Turkistan, Syria, Yemen and Iran. This engagement helped us shape and interpret our findings, and reflect on. 1) How should ethnicity be asked inclusively, and better recorded? 2) Does self-defined ethnicity change over time or context? If so, why?

RESULTS

Of the 44 included papers, most (19; 43%) used self-reported ethnicity, categorised in a variety of ways. Of the 27 papers that aggregated ethnicity, 13 (48%) provided justification. Only 8 of 33 papers explicitly theorised how ethnicity related to health. The focus groups agreed that 1) Ethnicity should not be prescribed by others; individuals could be asked to describe their ethnicity in free-text which researchers could synthesise to extract relevant dimensions of ethnicity for their research; 2) Ethnicity changes over time and context according to personal experience, social pressure, and nationality change; 3) Migrants and non-migrants' lived experience of ethnicity is not fully inter-changeable, even if they share the same ethnic category.

CONCLUSIONS

Ethnicity is a multi-dimensional construct, but this is not currently reflected in UK health research studies, where ethnicity is often aggregated and analysed without justification. Researchers should communicate clearly how ethnicity is operationalised for their study, with appropriate justification for clustering and analysis that is meaningfully theorised. We can only start to tackle ethnic health inequity by treating ethnicity as rigorously as any other variables in our research.

Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study

Background

Characterizing Post-Acute Sequelae of COVID (SARS-CoV-2 Infection), or PASC has been challenging due to the multitude of sub-phenotypes, temporal attributes, and definitions. Scalable characterization of PASC sub-phenotypes can enhance screening capacities, disease management, and treatment planning.

Methods

We conducted a retrospective multi-centre observational cohort study, leveraging longitudinal electronic health record (EHR) data of 30,422 patients from three healthcare systems in the Consortium for the Clinical Characterization of COVID-19 by EHR (4CE). From the total cohort, we applied a deductive approach on 12,424 individuals with follow-up data and developed a distributed representation learning process for providing augmented definitions for PASC sub-phenotypes.

Findings

Our framework characterized seven PASC sub-phenotypes. We estimated that on average 15.7% of the hospitalized COVID-19 patients were likely to suffer from at least one PASC symptom and almost 5.98%, on average, had multiple symptoms. Joint pain and dyspnea had the highest prevalence, with an average prevalence of 5.45% and 4.53%, respectively.

Interpretation

We provided a scalable framework to every participating healthcare system for estimating PASC sub-phenotypes prevalence and temporal attributes, thus developing a unified model that characterizes augmented sub-phenotypes across the different systems.

Funding

Authors are supported by National Institute of Allergy and Infectious Diseases, National Institute on Aging, National Center for Advancing Translational Sciences, National Medical Research Council, National Institute of Neurological Disorders and Stroke, European Union, National Institutes of Health, National Center for Advancing Translational Sciences.

Mapping and evaluating national data flows: transparency, privacy, and guiding infrastructural transformation

The importance of big health data is recognised worldwide. Most UK National Health Service (NHS) care interactions are recorded in electronic health records, resulting in an unmatched potential for population-level datasets. However, policy reviews have highlighted challenges from a complex data-sharing landscape relating to transparency, privacy, and analysis capabilities. In response, we used public information sources to map all electronic patient data flows across England, from providers to more than 460 subsequent academic, commercial, and public data consumers. Although NHS data support a global research ecosystem, we found that multistage data flow chains limit transparency and risk public trust, most data interactions do not fulfil recommended best practices for safe data access, and existing infrastructure produces aggregation of duplicate data assets, thus limiting diversity of data and added value to end users. We provide recommendations to support data infrastructure transformation and have produced a website (https://DataInsights.uk) to promote transparency and showcase NHS data assets.

Profiling post-COVID-19 condition across different variants of SARS-CoV-2: a prospective longitudinal study in unvaccinated wild-type, unvaccinated alpha-variant, and vaccinated delta-variant populations

BACKGROUND

Self-reported symptom studies rapidly increased understanding of SARS-CoV-2 during the COVID-19 pandemic and enabled monitoring of long-term effects of COVID-19 outside hospital settings. Post-COVID-19 condition presents as heterogeneous profiles, which need characterisation to enable personalised patient care. We aimed to describe post-COVID-19 condition profiles by viral variant and vaccination status.

METHODS

In this prospective longitudinal cohort study, we analysed data from UK-based adults (aged 18-100 years) who regularly provided health reports via the Covid Symptom Study smartphone app between March 24, 2020, and Dec 8, 2021. We included participants who reported feeling physically normal for at least 30 days before testing positive for SARS-CoV-2 who subsequently developed long COVID (ie, symptoms lasting longer than 28 days from the date of the initial positive test). We separately defined post-COVID-19 condition as symptoms that persisted for at least 84 days after the initial positive test. We did unsupervised clustering analysis of time-series data to identify distinct symptom profiles for vaccinated and unvaccinated people with post-COVID-19 condition after infection with the wild-type, alpha (B.1.1.7), or delta (B.1.617.2 and AY.x) variants of SARS-CoV-2. Clusters were then characterised on the basis of symptom prevalence, duration, demography, and previous comorbidities. We also used an additional testing sample with additional data from the Covid Symptom Study Biobank (collected between October, 2020, and April, 2021) to investigate the effects of the identified symptom clusters of post-COVID-19 condition on the lives of affected people.

FINDINGS

We included 9804 people from the COVID Symptom Study with long COVID, 1513 (15%) of whom developed post-COVID-19 condition. Sample sizes were sufficient only for analyses of the unvaccinated wild-type, unvaccinated alpha variant, and vaccinated delta variant groups. We identified distinct profiles of symptoms for post-COVID-19 condition within and across variants: four endotypes were identified for infections due to the wild-type variant (in unvaccinated people), seven for the alpha variant (in unvaccinated people), and five for the delta variant (in vaccinated people). Across all variants, we identified a cardiorespiratory cluster of symptoms, a central neurological cluster, and a multi-organ systemic inflammatory cluster. These three main clusers were confirmed in a testing sample. Gastrointestinal symptoms clustered in no more than two specific phenotypes per viral variant.

INTERPRETATION

Our unsupervised analysis identified different profiles of post-COVID-19 condition, characterised by differing symptom combinations, durations, and functional outcomes. Our classification could be useful for understanding the distinct mechanisms of post-COVID-19 condition, as well as for identification of subgroups of individuals who might be at risk of prolonged debilitation.

FUNDING

UK Government Department of Health and Social Care, Chronic Disease Research Foundation, The Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare, UK National Institute for Health Research, UK Medical Research Council, British Heart Foundation, UK Alzheimer's Society, and ZOE.

Biological and functional multimorbidity-from mechanisms to management

Globally, the number of people with multiple co-occurring diseases will increase substantially over the coming decades, with important consequences for patients, carers, healthcare systems and society. Addressing this challenge requires a shift in the prevailing clinical, educational and scientific thinking and organization-with a strong emphasis on the maintenance of generalist skills to balance the specialization trends of medical education and research. Multimorbidity is not a single entity but differs quantitively and qualitatively across life stages, ethnicities, sexes, socioeconomic groups and geographies. Data-driven science that quantifies the impact of disease co-occurrence-beyond the small number of currently well-studied long-term conditions (such as cardiometabolic diseases)-can help illuminate the pathological diversity of multimorbidity and identify common, mechanistically related, and prognostically relevant clusters. Broader access to data opportunities across modalities and disciplines will catalyze vertical and horizontal integration of multimorbidity research, to enable reconfiguring of medical services, clinical trials, guidelines and research in a way that accounts for the complexity of multimorbidity-and provides efficient, joined-up services for patients.

Systems analysis of human innate immunity in COVID-19

Recent developments in sequencing technologies, the computer and data sciences, as well as increasingly high-throughput immunological measurements have made it possible to derive holistic views on pathophysiological processes of disease and treatment effects directly in humans. We and others have illustrated that incredibly predictive data for immune cell function can be generated by single cell multi-omics (SCMO) technologies and that these technologies are perfectly suited to dissect pathophysiological processes in a new disease such as COVID-19, triggered by SARS-CoV-2 infection. Systems level interrogation not only revealed the different disease endotypes, highlighted the differential dynamics in context of disease severity, and pointed towards global immune deviation across the different arms of the immune system, but was already instrumental to better define long COVID phenotypes, suggest promising biomarkers for disease and therapy outcome predictions and explains treatment responses for the widely used corticosteroids. As we identified SCMO to be the most informative technologies in the vest to better understand COVID-19, we propose to routinely include such single cell level analysis in all future clinical trials and cohorts addressing diseases with an immunological component.

Missing data matter: an empirical evaluation of the impacts of missing EHR data in comparative effectiveness research

OBJECTIVES

The impacts of missing data in comparative effectiveness research (CER) using electronic health records (EHRs) may vary depending on the type and pattern of missing data. In this study, we aimed to quantify these impacts and compare the performance of different imputation methods.

MATERIALS AND METHODS

We conducted an empirical (simulation) study to quantify the bias and power loss in estimating treatment effects in CER using EHR data. We considered various missing scenarios and used the propensity scores to control for confounding. We compared the performance of the multiple imputation and spline smoothing methods to handle missing data.

RESULTS

When missing data depended on the stochastic progression of disease and medical practice patterns, the spline smoothing method produced results that were close to those obtained when there were no missing data. Compared to multiple imputation, the spline smoothing generally performed similarly or better, with smaller estimation bias and less power loss. The multiple imputation can still reduce study bias and power loss in some restrictive scenarios, eg, when missing data did not depend on the stochastic process of disease progression.

DISCUSSION AND CONCLUSION

Missing data in EHRs could lead to biased estimates of treatment effects and false negative findings in CER even after missing data were imputed. It is important to leverage the temporal information of disease trajectory to impute missing values when using EHRs as a data resource for CER and to consider the missing rate and the effect size when choosing an imputation method.

Changes in COVID-19-related mortality across key demographic and clinical subgroups in England from 2020 to 2022: a retrospective cohort study using the OpenSAFELY platform

BACKGROUND

COVID-19 has been shown to differently affect various demographic and clinical population subgroups. We aimed to describe trends in absolute and relative COVID-19-related mortality risks across clinical and demographic population subgroups during successive SARS-CoV-2 pandemic waves.

METHODS

We did a retrospective cohort study in England using the OpenSAFELY platform with the approval of National Health Service England, covering the first five SARS-CoV-2 pandemic waves (wave one [wild-type] from March 23 to May 30, 2020; wave two [alpha (B.1.1.7)] from Sept 7, 2020, to April 24, 2021; wave three [delta (B.1.617.2)] from May 28 to Dec 14, 2021; wave four [omicron (B.1.1.529)] from Dec 15, 2021, to April 29, 2022; and wave five [omicron] from June 24 to Aug 3, 2022). In each wave, we included people aged 18-110 years who were registered with a general practice on the first day of the wave and who had at least 3 months of continuous general practice registration up to this date. We estimated crude and sex-standardised and age-standardised wave-specific COVID-19-related death rates and relative risks of COVID-19-related death in population subgroups.

FINDINGS

18 895 870 adults were included in wave one, 19 014 720 in wave two, 18 932 050 in wave three, 19 097 970 in wave four, and 19 226 475 in wave five. Crude COVID-19-related death rates per 1000 person-years decreased from 4·48 deaths (95% CI 4·41-4·55) in wave one to 2·69 (2·66-2·72) in wave two, 0·64 (0·63-0·66) in wave three, 1·01 (0·99-1·03) in wave four, and 0·67 (0·64-0·71) in wave five. In wave one, the standardised COVID-19-related death rates were highest in people aged 80 years or older, people with chronic kidney disease stage 5 or 4, people receiving dialysis, people with dementia or learning disability, and people who had received a kidney transplant (ranging from 19·85 deaths per 1000 person-years to 44·41 deaths per 1000 person-years, compared with from 0·05 deaths per 1000 person-years to 15·93 deaths per 1000 person-years in other subgroups). In wave two compared with wave one, in a largely unvaccinated population, the decrease in COVID-19-related mortality was evenly distributed across population subgroups. In wave three compared with wave one, larger decreases in COVID-19-related death rates were seen in groups prioritised for primary SARS-CoV-2 vaccination, including people aged 80 years or older and people with neurological disease, learning disability, or severe mental illness (90-91% decrease). Conversely, smaller decreases in COVID-19-related death rates were observed in younger age groups, people who had received organ transplants, and people with chronic kidney disease, haematological malignancies, or immunosuppressive conditions (0-25% decrease). In wave four compared with wave one, the decrease in COVID-19-related death rates was smaller in groups with lower vaccination coverage (including younger age groups) and conditions associated with impaired vaccine response, including people who had received organ transplants and people with immunosuppressive conditions (26-61% decrease).

INTERPRETATION

There was a substantial decrease in absolute COVID-19-related death rates over time in the overall population, but demographic and clinical relative risk profiles persisted and worsened for people with lower vaccination coverage or impaired immune response. Our findings provide an evidence base to inform UK public health policy for protecting these vulnerable population subgroups.

FUNDING

UK Research and Innovation, Wellcome Trust, UK Medical Research Council, National Institute for Health and Care Research, and Health Data Research UK.

Predicting onset of disease progression using temporal disease occurrence networks

OBJECTIVE

Early recognition and prevention are crucial for reducing the risk of disease progression. This study aimed to develop a novel technique based on a temporal disease occurrence network to analyze and predict disease progression.

METHODS

This study used a total of 3.9 million patient records. Patient health records were transformed into temporal disease occurrence networks, and a supervised depth first search was used to find frequent disease sequences to predict the onset of disease progression. The diseases represented nodes in the network and paths between nodes represented edges that co-occurred in a patient cohort with temporal order. The node and edge level attributes contained meta-information about patients' gender, age group, and identity as labels where the disease occurred. The node and edge level attributes guided the depth first search to identify frequent disease occurrences in specific genders and age groups. The patient history was used to match the most frequent disease occurrences and then the obtained sequences were merged together to generate a ranked list of diseases with their conditional probability and relative risk.

RESULTS

The study found that the proposed method had improved performance compared to other methods. Specifically, when predicting a single disease, the method achieved an area under the receiver operating characteristic curve (AUC) of 0.65 and an F1-score of 0.11. When predicting a set of diseases relative to ground truth, the method achieved an AUC of 0.68 and an F1-score of 0.13.

CONCLUSION

The ranked list generated by the proposed method, which includes the probability of occurrence and relative risk score, can provide physicians with valuable information about the sequential development of diseases in patients. This information can help physicians to take preventive measures in a timely manner, based on the best available information.

The COVID-19 Vaccination Coverage in ICU Patients with Severe COVID-19 Infection in a Country with Low Vaccination Coverage-A National Retrospective Analysis

BACKGROUND

Romania is one of the European countries with low COVID-19 vaccination coverage. The main goal of this study was to describe the COVID-19 vaccination status in patients admitted to Romanian ICUs with a severe COVID-19 infection. The study describes the patients' characteristics according to their vaccination status and evaluates the association between vaccination status and ICU mortality.

METHODS

This retrospective, observational, multicenter study included patients with confirmed vaccination status admitted to Romanian ICUs from January 2021 to March 2022.

RESULTS

Two thousand, two hundred and twenty-two patients with confirmed vaccination status were included. Five point one three percent of patients were vaccinated with two vaccine doses and one point seventeen percent of patients were vaccinated with one vaccine dose. The vaccinated patients showed a higher rate of comorbidities but had similar clinical characteristics at ICU admission and lower mortality rates compared to non-vaccinated patients. Vaccinated status and higher Glasgow Coma Scale at ICU admission were independently associated with ICU survival. Ischemic heart disease, chronic kidney disease, higher SOFA score at ICU admission and the need for mechanical ventilation in ICU were independently associated with ICU mortality.

CONCLUSION

Lower rates of ICU admission were observed in fully vaccinated patients even in a country with low vaccination coverage. The ICU mortality was lower for fully vaccinated patients compared to non-vaccinated patients. The benefit of vaccination on ICU survival could be more important in patients with associated comorbidities.

Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19-a data-driven retrospective cohort study

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.

COVID-19 in people aged 18-64 in Sweden in the first year of the pandemic: Key factors for severe disease and death

Background

Studies on risk factors for severe COVID-19 in people of working age have generally not included non-working persons or established population attributable fractions (PAFs) for occupational and other factors.

Objectives

We describe the effect of job-related, sociodemographic, and other exposures on the incidence, relative risks and PAFs of severe COVID-19 in individuals aged 18-64.

Methods

We conducted a registry-based study in Swedish citizens aged 18-64 from 1 January 2020 to 1 February 2021 with respect to COVID-19-related hospitalizations and death.

Results

Of 6,205,459 persons, 272,043 (7.5%) were registered as infected, 3399 (0.05%) needed intensive care, and 620 (0.01%) died, with an estimated case fatality rate of 0.06% over the last 4-month period when testing was adequate. Non-Nordic origin was associated with a RR for need of intensive care of 3·13, 95%CI 2·91-3·36, and a PAF of 32·2% after adjustment for age, sex, work, region and comorbidities. In a second model with occupation as main exposure, and adjusted for age, sex, region, comorbidities and origin, essential workers had an RR of 1·51, 95%CI, 1·35-1·6, blue-collar workers 1·18, 95%CI 1·06-1·31, school staff 1·21, 95%CI 1·01-1·46, and health and social care workers 1·89, 95%CI 1·67-2·135) compared with people able to work from home, with altogether about 13% of the PAF associated with these occupations. Essential workers and blue-collar workers, but no other job categories had higher risk of death, adjusted RRs of 1·79, 95%CI 1·34-2·38 and 1·37, 95%CI 1·04-1·81, with adjusted PAFs of altogether 9%.

Conclusion

Among people of working age in Sweden, overall mortality and case fatality were low. Occupations that require physical presence at work were associated with elevated risk of needing intensive care for COVID-19, with 14% cases attributable to this factor, and 9% of deaths.

Association of COVID-19 With Major Arterial and Venous Thrombotic Diseases: A Population-Wide Cohort Study of 48 Million Adults in England and Wales

BACKGROUND

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces a prothrombotic state, but long-term effects of COVID-19 on incidence of vascular diseases are unclear.

METHODS

We studied vascular diseases after COVID-19 diagnosis in population-wide anonymized linked English and Welsh electronic health records from January 1 to December 7, 2020. We estimated adjusted hazard ratios comparing the incidence of arterial thromboses and venous thromboembolic events (VTEs) after diagnosis of COVID-19 with the incidence in people without a COVID-19 diagnosis. We conducted subgroup analyses by COVID-19 severity, demographic characteristics, and previous history.

RESULTS

Among 48 million adults, 125 985 were hospitalized and 1 319 789 were not hospitalized within 28 days of COVID-19 diagnosis. In England, there were 260 279 first arterial thromboses and 59 421 first VTEs during 41.6 million person-years of follow-up. Adjusted hazard ratios for first arterial thrombosis after COVID-19 diagnosis compared with no COVID-19 diagnosis declined from 21.7 (95% CI, 21.0-22.4) in week 1 after COVID-19 diagnosis to 1.34 (95% CI, 1.21-1.48) during weeks 27 to 49. Adjusted hazard ratios for first VTE after COVID-19 diagnosis declined from 33.2 (95% CI, 31.3-35.2) in week 1 to 1.80 (95% CI, 1.50-2.17) during weeks 27 to 49. Adjusted hazard ratios were higher, for longer after diagnosis, after hospitalized versus nonhospitalized COVID-19, among Black or Asian versus White people, and among people without versus with a previous event. The estimated whole-population increases in risk of arterial thromboses and VTEs 49 weeks after COVID-19 diagnosis were 0.5% and 0.25%, respectively, corresponding to 7200 and 3500 additional events, respectively, after 1.4 million COVID-19 diagnoses.

CONCLUSIONS

High relative incidence of vascular events soon after COVID-19 diagnosis declines more rapidly for arterial thromboses than VTEs. However, incidence remains elevated up to 49 weeks after COVID-19 diagnosis. These results support policies to prevent severe COVID-19 by means of COVID-19 vaccines, early review after discharge, risk factor control, and use of secondary preventive agents in high-risk patients.

A retrospective cohort study predicting and validating impact of the COVID-19 pandemic in individuals with chronic kidney disease

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.