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

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

Effect of remote ischaemic conditioning on clinical outcomes in patients with acute myocardial infarction (CONDI-2/ERIC-PPCI): a single-blind randomised controlled trial.

BACKGROUND: Remote ischaemic conditioning with transient ischaemia and reperfusion applied to the arm has been shown to reduce myocardial infarct size in patients with ST-elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI). We investigated whether remote ischaemic conditioning could reduce the incidence of cardiac death and hospitalisation for heart failure at 12 months. METHODS: We did an international investigator-initiated, prospective, single-blind, randomised controlled trial (CONDI-2/ERIC-PPCI) at 33 centres across the UK, Denmark, Spain, and Serbia. Patients (age >18 years) with suspected STEMI and who were eligible for PPCI were randomly allocated (1:1, stratified by centre with a permuted block method) to receive standard treatment (including a sham simulated remote ischaemic conditioning intervention at UK sites only) or remote ischaemic conditioning treatment (intermittent ischaemia and reperfusion applied to the arm through four cycles of 5-min inflation and 5-min deflation of an automated cuff device) before PPCI. Investigators responsible for data collection and outcome assessment were masked to treatment allocation. The primary combined endpoint was cardiac death or hospitalisation for heart failure at 12 months in the intention-to-treat population. This trial is registered with ClinicalTrials.gov (NCT02342522) and is completed. FINDINGS: Between Nov 6, 2013, and March 31, 2018, 5401 patients were randomly allocated to either the control group (n=2701) or the remote ischaemic conditioning group (n=2700). After exclusion of patients upon hospital arrival or loss to follow-up, 2569 patients in the control group and 2546 in the intervention group were included in the intention-to-treat analysis. At 12 months post-PPCI, the Kaplan-Meier-estimated frequencies of cardiac death or hospitalisation for heart failure (the primary endpoint) were 220 (8·6%) patients in the control group and 239 (9·4%) in the remote ischaemic conditioning group (hazard ratio 1·10 [95% CI 0·91-1·32], p=0·32 for intervention versus control). No important unexpected adverse events or side effects of remote ischaemic conditioning were observed. INTERPRETATION: Remote ischaemic conditioning does not improve clinical outcomes (cardiac death or hospitalisation for heart failure) at 12 months in patients with STEMI undergoing PPCI. FUNDING: British Heart Foundation, University College London Hospitals/University College London Biomedical Research Centre, Danish Innovation Foundation, Novo Nordisk Foundation, TrygFonden

Ethnic inequalities in health-related quality of life amongst older adults in England: secondary analysis of a national cross-sectional survey:secondary analysis of a national cross-sectional survey

BACKGROUND: The population of older adults (ie, those aged ≥55 years) in England is becoming increasingly ethnically diverse. Previous reports indicate that ethnic inequalities in health exist among older adults, but information is limited by the paucity of data from small minority ethnic groups. This study aimed to analyse inequalities in health-related quality of life (HRQoL) and five determinants of health in older adults across all ethnic groups in England. METHODS: In this cross-sectional study, we analysed data from five waves (July 1, 2014, to April 7, 2017) of the nationally representative English General Practice Patient Survey (GPPS). Study participants were adults aged 55 years or older who were registered with general practices in England. We used regression models (age-adjusted and stratified by gender) to estimate the association between ethnicity and HRQoL, measured by use of the EQ-5D-5L index and its domains (mobility, self-care, usual activities, pain or discomfort, and anxiety or depression). We also estimated associations between ethnicity and five determinants of health (presence of long-term conditions or multimorbidity, experience of primary care, degree of support from local services, patient self-confidence in managing own health, and degree of area-level social deprivation). We examined robustness to differential handling of missing data, alternative EQ-5D-5L value sets, and differences in area-level social deprivation. FINDINGS: There were 1 416 793 GPPS respondents aged 55 years and older. 1 394 361 (98·4%) respondents had complete data on ethnicity and gender and were included in our analysis. Of these, 152 710 (11·0%) self-identified as belonging to minority ethnic groups. HRQoL was worse for men or women, or both, in 15 (88·2%) of 17 minority ethnic groups than the White British ethnic group. In both men and women, inequalities were widest for Gypsy or Irish Traveller (linear regression coefficient -0·192 [95% CI -0·318 to -0·066] in men; -0·264 [-0·354 to -0·173] in women), Bangladeshi (-0·111 [-0·136 to -0·087] in men; -0·209 [-0·235 to -0·184] in women), Pakistani (-0·084 [-0·096 to -0·073] in men; -0·206 [-0·219 to -0·193] in women), and Arab (-0·061 [-0·086 to -0·035] in men; -0·145 [-0·180 to -0·110] in women) ethnic groups, with magnitudes generally greater for women than men. Differentials tended to be widest for the self-care EQ-5D-5L domain. Ethnic inequalities in HRQoL were accompanied by increased prevalence of long-term conditions or multimorbidity, poor experiences of primary care, insufficient support from local services, low patient self-confidence in managing their own health, and high area-level social deprivation, compared with the White British group. INTERPRETATION: We found evidence of wide ethnic inequalities in HRQoL and five determinants of health for older adults in England. Outcomes varied between minority ethnic groups, highlighting heterogeneity in the direction and magnitude of associations. We recommend further research to understand the drivers of inequalities, together with policy changes to improve equity of socioeconomic opportunity and access to services for older adults from minority ethnic groups. FUNDING: University of Manchester and National Institute for Health Research

Gender-related self-reported mental health inequalities in primary care in England: Cross-sectional analysis using the GP Patient Survey

BackgroundTrans, non-binary, and gender diverse people face discrimination and barriers to healthcare. Existing evidence suggests higher rates of mental health conditions amongst these groups compared to binary, cisgender groups. However, information is limited by poor gender recording in health records and surveys. We aimed to provide the first national estimates of gender-related inequalities in self-reported mental health conditions and mental health support across 15 gender groups in England.MethodsWe exploited changes to the 2021 and 2022 nationally representative cross-sectional English GP Patient Surveys and used age-adjusted logistic regression to predict probabilities of two outcomes (1) self-reporting a mental health condition and (2) self-reporting unmet mental health needs. We report results for 15 exposure groups: five gender groups (Female, Male, Non-binary, Prefer to self-describe, Prefer not to say), each within three cis/trans identity groups (Cisgender, Transgender, Prefer not to say). We explored potential mediation by adding covariates.FindingsOf the 1,520,457 respondents in the estimation sample, 861,017 (51.4%) were female, 645,300 (47·4%) were male, 2,600 (0·3%) were non-binary, 2,277 (0·2%) self-described their gender, and 9,263 (0·7%) preferred not to state their gender. 1,499,852 (98·3%) respondents were cisgender, 7,994 (0·7%) were transgender, and 12,611 (1.0%) preferred not to say their cis/trans identity. We found wide gender-related inequalities in the probability of self-reporting a mental health condition, with the highest probabilities amongst non-binary patients who were transgender (47·21% [95% CI 42·86%-51·60%]) or preferred not to say their cis/trans identity (32·90% [26·50%-40·00%]), and amongst transgender patients who self-described their gender (35·03% [27·39%-43·53%]). With the exception of non-binary patients in each case, probabilities were lowest amongst cisgender patient groups (range: male 8·80% [8·69%-8·92%] to female 11·97% [11·86%-12·07%]) and patients who preferred not to say their cis/trans identity (range: female 7·15% [6·06%-8·42%] to prefer to self-describe 10·37% [7·13%-14·86%]). Inequalities in other health conditions and socioeconomic factors may mediate some of these inequalities. Probabilities of self-reported unmet mental health needs were lowest amongst cisgender male (15·55% [95% CI 15·33%-15·76%]) and female (15·93% [15·76%-16·10%]) patients with increased probabilities amongst all other groups, ranging from 19.95% [17·57%-22·57%] amongst transgender male patients to 28·64% [26·23%-31·17%] amongst patients who preferred not to say their gender and cis/trans identity. Inequalities in interactions with healthcare professionals may mediate much of these inequalities.InterpretationTogether with existing evidence, our findings showed large gender-related inequalities in self-reported mental health outcomes in England. Given the existence of self-reported unmet mental health needs, we suggest that better healthcare system inclusivity and healthcare professional training are needed, alongside broader improvements <br/

Correction:Ethnic inequalities in COVID-19 vaccine uptake and comparison to seasonal influenza vaccine uptake in Greater Manchester, UK: A cohort study (PLoS Med 19: 3 (e100393)2 DOI: 10.1371/journal.pmed.1003932)

There is an error in the Funding Disclosure. The correct Funding Disclosure reads: This work was funded by an internal grant from the University of Manchester. University of Manchester website: https://www.manchester.ac.uk/. CS and MS and the PCIE input were part-funded by the National Institute for Health Research (NIHR) Applied Research Collaboration Greater Manchester (award number: NIHR200174). MS is a NIHR Senior Investigator. CS was part-funded and RW was funded by the NIHR Greater Manchester Patient Safety Translational Research Centre (award number: PSTRC-2016-003). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.</p

Evaluating socioeconomic inequalities in influenza vaccine uptake during the COVID-19 pandemic: A cohort study in Greater Manchester, England.

BackgroundThere are known socioeconomic inequalities in annual seasonal influenza (flu) vaccine uptake. The Coronavirus Disease 2019 (COVID-19) pandemic was associated with multiple factors that may have affected flu vaccine uptake, including widespread disruption to healthcare services, changes to flu vaccination eligibility and delivery, and increased public awareness and debate about vaccination due to high-profile COVID-19 vaccination campaigns. However, to the best of our knowledge, no existing studies have investigated the consequences for inequalities in flu vaccine uptake, so we aimed to investigate whether socioeconomic inequalities in flu vaccine uptake have widened since the onset of the COVID-19 pandemic.Methods and findingsWe used deidentified data from electronic health records for a large city region (Greater Manchester, population 2.8 million), focusing on 3 age groups eligible for National Health Service (NHS) flu vaccination: preschool children (age 2 to 3 years), primary school children (age 4 to 9 years), and older adults (age 65 years plus). The sample population varied between 418,790 (2015/16) and 758,483 (2021/22) across each vaccination season. We estimated age-adjusted neighbourhood-level income deprivation-related inequalities in flu vaccine uptake using Cox proportional hazards models and the slope index of inequality (SII), comparing 7 flu vaccination seasons (2015/16 to 2021/22). Among older adults, the SII (i.e., the gap in uptake between the least and most income-deprived areas) doubled over the 7 seasons from 8.48 (95% CI [7.91,9.04]) percentage points to 16.91 (95% CI [16.46,17.36]) percentage points, with approximately 80% of this increase occurring during the pandemic. Before the pandemic, income-related uptake gaps were wider among children, ranging from 15.59 (95% CI [14.52,16.67]) percentage points to 20.07 (95% CI [18.94,21.20]) percentage points across age groups and vaccination seasons. Among preschool children, the uptake gap increased in 2020/21 to 25.25 (95% CI [24.04,26.45]) percentage points, before decreasing to 20.86 (95% CI [19.65,22.05]) percentage points in 2021/22. Among primary school children, inequalities increased in both pandemic years to reach 30.27 (95% CI [29.58,30.95]) percentage points in 2021/22. Although vaccine uptake increased during the pandemic, disproportionately larger increases in uptake in less deprived areas created wider inequalities in all age groups. The main limitation of our approach is the use of a local dataset, which may limit generalisability to other geographical settings.ConclusionsThe COVID-19 pandemic led to increased inequalities in flu vaccine uptake, likely due to changes in demand for vaccination, new delivery models, and disruptions to healthcare and schooling. It will be important to investigate the causes of these increased inequalities and to examine whether these increased inequalities also occurred in the uptake of other routine vaccinations. These new wider inequalities in flu vaccine uptake may exacerbate inequalities in flu-related morbidity and mortality

Ethnic inequalities in COVID-19 vaccine uptake and comparison to seasonal influenza vaccine uptake in Greater Manchester, UK: A cohort study.

BackgroundCOVID-19 vaccine uptake is lower amongst most minority ethnic groups compared to the White British group in England, despite higher COVID-19 mortality rates. Here, we add to existing evidence by estimating inequalities for 16 minority ethnic groups, examining ethnic inequalities within population subgroups, and comparing the magnitudes of ethnic inequalities in COVID-19 vaccine uptake to those for routine seasonal influenza vaccine uptake.Methods and findingsWe conducted a retrospective cohort study using the Greater Manchester Care Record, which contains de-identified electronic health record data for the population of Greater Manchester, England. We used Cox proportional hazards models to estimate ethnic inequalities in time to COVID-19 vaccination amongst people eligible for vaccination on health or age (50+ years) criteria between 1 December 2020 and 18 April 2021 (138 days of follow-up). We included vaccination with any approved COVID-19 vaccine, and analysed first-dose vaccination only. We compared inequalities between COVID-19 and influenza vaccine uptake adjusting by age group and clinical risk, and used subgroup analysis to identify populations where inequalities were widest. The majority of individuals (871,231; 79.24%) were White British. The largest minority ethnic groups were Pakistani (50,268; 4.75%), 'other White background' (43,195; 3.93%), 'other ethnic group' (34,568; 3.14%), and Black African (18,802; 1.71%). In total, 83.64% (919,636/1,099,503) of eligible individuals received a COVID-19 vaccine. Uptake was lower compared to the White British group for 15 of 16 minority ethnic groups, with particularly wide inequalities amongst the groups 'other Black background' (hazard ratio [HR] 0.42, 95% CI 0.40 to 0.44), Black African (HR 0.43, 95% CI 0.42 to 0.44), Arab (HR 0.43, 95% CI 0.40 to 0.48), and Black Caribbean (HR 0.43, 95% CI 0.42 to 0.45). In total, 55.71% (419,314/752,715) of eligible individuals took up influenza vaccination. Compared to the White British group, inequalities in influenza vaccine uptake were widest amongst the groups 'White and Black Caribbean' (HR 0.63, 95% CI 0.58 to 0.68) and 'White and Black African' (HR 0.67, 95% CI 0.63 to 0.72). In contrast, uptake was slightly higher than the White British group amongst the groups 'other ethnic group' (HR 1.11, 95% CI 1.09 to 1.12) and Bangladeshi (HR 1.08, 95% CI 1.05 to 1.11). Overall, ethnic inequalities in vaccine uptake were wider for COVID-19 than influenza vaccination for 15 of 16 minority ethnic groups. COVID-19 vaccine uptake inequalities also existed amongst individuals who previously took up influenza vaccination. Ethnic inequalities in COVID-19 vaccine uptake were concentrated amongst older and extremely clinically vulnerable adults, and the most income-deprived. A limitation of this study is the focus on uptake of the first dose of COVID-19 vaccination, rather than full COVID-19 vaccination.ConclusionsEthnic inequalities in COVID-19 vaccine uptake exceeded those for influenza vaccine uptake, existed amongst those recently vaccinated against influenza, and were widest amongst those with greatest COVID-19 risk. This suggests the COVID-19 vaccination programme has created additional and different inequalities beyond pre-existing health inequalities. We suggest that further research and policy action is needed to understand and remove barriers to vaccine uptake, and to build trust and confidence amongst minority ethnic communities