Ethnic differences in COVID-19 mortality during the first two waves of the Coronavirus Pandemic: a nationwide cohort study of 29 million adults in England (preprint)

Background Ethnic minorities have experienced disproportionate COVID-19 mortality rates in the UK and many other countries. We compared the differences in the risk of COVID-19 related death between ethnic groups in the first and second waves the of COVID-19 pandemic in England. We also investigated whether the factors explaining differences in COVID-19 death between ethnic groups changed between the two waves. Methods Using data from the Office for National Statistics Public Health Data Asset on individuals aged 30-100 years living in private households, we conducted an observational cohort study to examine differences in the risk of death involving COVID-19 between ethnic groups in the first wave (from 24 th January 2020 until 31 st August 2020) and second wave (from 1 st September to 28 th December 2020). We estimated age-standardised mortality rates (ASMR) in the two waves stratified by ethnic groups and sex. We also estimated hazard ratios (HRs) for ethnic-minority groups compared with the White British population, adjusted for geographical factors, socio-demographic characteristics, and pre-pandemic health conditions. Results The study population included over 28.9 million individuals aged 30-100 years living in private households. In the first wave, all ethnic minority groups had a higher risk of COVID-19 related death compared to the White British population. In the second wave, the risk of COVID-19 death remained elevated for people from Pakistani (ASMR: 339.9 [95% CI: 303.7 – 376.2] and 166.8 [141.7 – 191.9] deaths per 100,000 population in men and women) and Bangladeshi (318.7 [247.4 – 390.1] and 127.1 [91.1 – 171.3] in men and women)background but not for people from Black ethnic groups. Adjustment for geographical factors explained a large proportion of the differences in COVID-19 mortality in the first wave but not in the second wave. Despite an attenuation of the elevated risk of COVID-19 mortality after adjusting for sociodemographic characteristics and health status, the risk was substantially higher in people from Bangladeshi and Pakistani background in both the first and the second waves. Conclusion Between the first and second waves of the pandemic, the reduction in the difference in COVID-19 mortality between people from Black ethnic background and people from the White British group shows that ethnic inequalities in COVID-19 mortality can be addressed. The continued higher rate of mortality in people from Bangladeshi and Pakistani background is alarming and requires focused public health campaign and policy changes. *VN and NI contributed equally to this paper Research in context Evidence before this study A recent systematic review by Pan and colleagues demonstrated that people of ethnic minority background in the UK and the USA have been disproportionately affected by the Coronavirus (COVID-19) pandemic, compared to White populations. While several studies have investigated whether adjusting for socio-demographic and economic factors and medical history reduces the estimated difference in risk of mortality and hospitalisation, the reasons for the differences in the risk of experiencing harms from COVID-19 are still being explored during the course of the pandemic. Studies so far have analysed the ethnic differences in COVID-19 mortality in the first wave of the pandemic. The evidence on the temporal trend of ethnic inequalities in COVID-19 mortality, especially those from the second wave of the pandemic, is scarce. Added value of this study Using data from the Office for National Statistics (ONS) Public Health Data Asset on 29 million adults aged 30-100 years living in private households in England, we conducted an observational cohort study to examine the differences in the risk of death involving COVID-19 between ethnic groups in the first wave (from 24 th January 2020 until 31 st August 2020) and second wave (from 1 st September to 28 th December 2020). We find that in the first wave all ethnic minority groups were at elevated risk of COVID-19 related death compared to the White British population. In the second wave, the differences in the risk of COVID-19 related death attenuated for Black African and Black Caribbean groups, remained substantially higher in people from Bangladeshi background, and worsened in people from Pakistani background. We also find that some of the factors explaining these differences in mortality have changed in the two waves. Implications of all the available evidence The risk of COVID-19 mortality during the first wave of the pandemic was elevated in people from ethnic minority background. An appreciable reduction in the difference in COVID-19 mortality in the second wave of the pandemic between people from Black ethnic background and people from the White British group is reassuring, but the continued higher rate of mortality in people from Bangladeshi and Pakistani background is alarming and requires focused public health campaign and policy response. Focusing on treating underlying conditions, although important, may not be enough in reducing the inequalities in COVID-19 mortality. Focused public health policy as well as community mobilisation and participatory public health campaign involving community leaders may help reduce the existing and widening inequalities in COVID-19 mortality.

An external validation of the QCovid risk prediction algorithm for risk of mortality from COVID-19 in adults: national validation cohort study in England (preprint)

BackgroundTo externally validate a risk prediction algorithm (QCovid) to estimate mortality outcomes from COVID-19 in adults in England. MethodsPopulation-based cohort study using the ONS Public Health Linked Data Asset, a cohort based on the 2011 Census linked to Hospital Episode Statistics, the General Practice Extraction Service Data for pandemic planning and research, radiotherapy and systemic chemotherapy records. The primary outcome was time to COVID-19 death, defined as confirmed or suspected COVID-19 death as per death certification. Two time periods were used: (a) 24th January to 30th April 2020; and (b) 1st May to 28th July 2020. We evaluated the performance of the QCovid algorithms using measures of discrimination and calibration for each validation time period. FindingsThe study comprises 34,897,648 adults aged 19-100 years resident in England. There were 26,985 COVID-19 deaths during the first time-period and 13,177 during the second. The algorithms had good calibration in the validation cohort in both time periods with close correspondence of observed and predicted risks. They explained 77.1% (95% CI: 76.9% to 77.4%) of the variation in time to death in men in the first time-period (R2); the D statistic was 3.76 (95% CI: 3.73 to 3.79); Harrells C was 0.935 (0.933 to 0.937). Similar results were obtained for women, and in the second time-period. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths in the first time period was 65.9% for men and 71.7% for women. People in the top 20% of predicted risks of death accounted for 90.8% of all COVID-19 deaths for men and 93.0% for women. InterpretationThe QCovid population-based risk algorithm performed well, showing very high levels of discrimination for COVID-19 deaths in men and women for both time periods. It has the potential to be dynamically updated as the pandemic evolves and therefore, has potential use in guiding national policy. FundingNational Institute of Health Research RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSPublic policy measures and clinical risk assessment relevant to COVID-19 need to be aided by rigorously developed and validated risk prediction models. A recent living systematic review of published risk prediction models for COVID-19 found most models are subject to a high risk of bias with optimistic reported performance, raising concern that these models may be unreliable when applied in practice. A population-based risk prediction model, QCovid risk prediction algorithm, has recently been developed to identify adults at high risk of serious COVID-19 outcomes, which overcome many of the limitations of previous tools. Added value of this studyCommissioned by the Chief Medical Officer for England, we validated the novel clinical risk prediction model (QCovid) to identify risks of short-term severe outcomes due to COVID-19. We used national linked datasets from general practice, death registry and hospital episode data for a population-representative sample of over 34 million adults. The risk models have excellent discrimination in men and women (Harrells C statistic>0.9) and are well calibrated. QCovid represents a new, evidence-based opportunity for population risk-stratification. Implications of all the available evidenceQCovid has the potential to support public health policy, from enabling shared decision making between clinicians and patients in relation to health and work risks, to targeted recruitment for clinical trials, and prioritisation of vaccination, for example.

Epidemiology of post-COVID syndrome following hospitalisation with coronavirus: a retrospective cohort study (preprint)

Objectives The epidemiology of post-COVID syndrome (PCS) is currently undefined. We quantified rates of organ-specific impairment following recovery from COVID-19 hospitalisation compared with those in a matched control group, and how the rate ratio (RR) varies by age, sex, and ethnicity. Design Observational, retrospective, matched cohort study. Setting NHS hospitals in England. Participants 47,780 individuals (mean age 65 years, 55% male) in hospital with COVID-19 and discharged alive by 31 August 2020, matched to controls on demographic and clinical characteristics. Outcome measures Rates of hospital readmission, all-cause mortality, and diagnoses of respiratory, cardiovascular, metabolic, kidney and liver diseases until 30 September 2020. Results Mean follow-up time was 140 days for COVID-19 cases and 153 days for controls. 766 (95% confidence interval: 753 to 779) readmissions and 320 (312 to 328) deaths per 1,000 person-years were observed in COVID-19 cases, 3.5 (3.4 to 3.6) and 7.7 (7.2 to 8.3) times greater, respectively, than in controls. Rates of respiratory, diabetes and cardiovascular events were also significantly elevated in COVID-19 cases, at 770 (758 to 783), 127 (122 to 132) and 126 (121 to 131) events per 1,000 person-years, respectively. RRs were greater for individuals aged <70 than ≥ 70 years, and in ethnic minority groups than the White population, with the biggest differences observed for respiratory disease: 10.5 [9.7 to 11.4] for <70 years versus 4.6 [4.3 to 4.8] for ≥ 70 years, and 11.4 (9.8 to 13.3) for Non-White versus 5.2 (5.0 to 5.5) for White. Conclusions Individuals discharged from hospital following COVID-19 face elevated rates of multi-organ dysfunction compared with background levels, and the increase in risk is neither confined to the elderly nor uniform across ethnicities. The diagnosis, treatment and prevention of PCS require integrated rather than organ- or disease-specific approaches. Urgent research is required to establish risk factors for PCS.

Ethnic minority groups in England and Wales - factors affecting the size and timing of elevated COVID-19 mortality: a retrospective cohort study linking Census and death records (preprint)

Objectives: To estimate population-level associations between ethnicity and coronavirus disease 2019 (COVID-19) mortality, and to investigate how ethnicity-specific mortality risk evolved over the course of the pandemic. Design: Retrospective cohort study using linked administrative data. Setting: England and Wales, deaths occurring 2 March to 15 May 2020. Participants: Respondents to the 2011 Census of England and Wales aged [≤]100 years and enumerated in private households, linked to death registrations and adjusted to account for emigration before the outcome period, who were alive on 1 March 2020 (n=47,872,412). Main outcome measure: Death related to COVID-19, registered by 29 May 2020. Statistical methods: We estimated hazard ratios (HRs) for ethnic minority groups compared with the White population using Cox regression models, controlling for geographical, demographic, socio-economic, occupational, and self-reported health factors. HRs were estimated on the full outcome period and separately for pre- and post-lockdown periods in the UK. Results: In the age-adjusted models, people from all ethnic minority groups were at elevated risk of COVID-19 mortality; the HRs for Black males and females were 3.13 [95% confidence interval: 2.93 to 3.34] and 2.40 [2.20 to 2.61] respectively. However, in the fully adjusted model for females, the HRs were close to unity for all ethnic groups except Black (1.29 [1.18 to 1.42]). For males, COVID-19 mortality risk remained elevated for the Black (1.76 [1.63 to 1.90]), Bangladeshi/Pakistani (1.35 [1.21 to 1.49]) and Indian (1.30 [1.19 to 1.43]) groups. The HRs decreased after lockdown for all ethnic groups, particularly Black and Bangladeshi/Pakistani females. Conclusions: Differences in COVID-19 mortality between ethnic groups were largely attenuated by geographical and socio-economic factors, although some residual differences remained. Lockdown was associated with reductions in excess mortality risk in ethnic minority populations, which has major implications for a second wave of infection or local spikes. Further research is needed to understand the causal mechanisms underpinning observed differences in COVID-19 mortality between ethnic groups.

Place and causes of acute cardiovascular mortality during the COVID19 pandemic: retrospective cohort study of 580,972 deaths in England and Wales, 2014 to 2020 (preprint)

Importance. The COVID-19 pandemic has resulted in a decline in admissions with cardiovascular (CV) emergencies. The fatal consequences of this are unknown. Objectives - To describe the place and causes of acute CV death during the COVID-19 pandemic. Design - Retrospective nationwide cohort. Setting - England and Wales. Participants - All adult (age [≥]18 years) acute CV deaths (n=580,972) between 1st January 2014 and 2nd June 2020. Exposure - The COVID-19 pandemic (defined as from the onset of the first COVID-19 death in England on 2nd March 2020). Main outcomes - Place (hospital, care home, home) and acute CV events directly contributing to death as stated on the first part of the Medical Certificate of Cause of Death. Results - After 2nd March 2020, there were 22,820 acute CV deaths of which 5.7% related to COVID-19, and an excess acute CV mortality of 1752 (+8%) compared with the expected daily deaths in the same period. Deaths in the community accounted for nearly half of all deaths during this period. Care homes had the greatest increase in excess acute CV deaths (1065, +40%), followed by deaths at home (1728, +34%) and in hospital (57, +0%). The most frequent cause of acute CV death during this period was stroke (8,290, 36.3%), followed by acute coronary syndrome (ACS) (5,532, 24.2%), heart failure (5,280, 23.1%), pulmonary embolism (2,067, 9.1%) and cardiac arrest (1,037, 4.5%). Deep vein thrombosis had the greatest increase in cause of excess acute CV death (18, +25%), followed pulmonary embolism (340, +19%) and stroke (782, +10%). The greatest cause of excess CV death in care homes was stroke (700, +48%), compared with cardiac arrest (80, +56%) at home, and pulmonary embolism (126, +14%) and cardiogenic shock (41, +14%) in hospital. Conclusions and relevance - The COVID-19 pandemic has resulted in an inflation in acute CV deaths above that expected for the time of year, nearly half of which occurred in the community. The most common cause of acute CV death was stroke followed by acute coronary syndrome and heart failure. This is key information to optimise messaging to the public and enable health resource planning.

Excess deaths in people with cardiovascular diseases during the COVID-19 pandemic. (preprint)

Background: Cardiovascular diseases(CVD) increase mortality risk from coronavirus infection(COVID-19), but there are concerns that the pandemic has affected supply and demand of acute cardiovascular care. We estimated excess mortality in specific CVDs, both direct, through infection, and indirect, through changes in healthcare. Methods: We used population-based electronic health records from 3,862,012 individuals in England to estimate pre- and post-COVID-19 mortality risk(direct effect) for people with incident and prevalent CVD. We incorporated: (i)pre-COVID-19 risk by age, sex and comorbidities, (ii)estimated population COVID-19 prevalence, and (iii)estimated relative impact of COVID-19 on mortality(relative risk, RR: 1.5, 2.0 and 3.0). For indirect effects, we analysed weekly mortality and emergency department data for England/Wales and monthly hospital data from England(n=2), China(n=5) and Italy(n=1) for CVD referral, diagnosis and treatment until 1 May 2020. Findings: CVD service activity decreased by 60-100% compared with pre-pandemic levels in eight hospitals across China, Italy and England during the pandemic. In China, activity remained below pre-COVID-19 levels for 2-3 months even after easing lockdown, and is still reduced in Italy and England. Mortality data suggest indirect effects on CVD will be delayed rather than contemporaneous(peak RR 1.4). For total CVD(incident and prevalent), at 10% population COVID-19 rate, we estimated direct impact of 31,205 and 62,410 excess deaths in England at RR 1.5 and 2.0 respectively, and indirect effect of 49932 to 99865 excess deaths. Interpretation: Supply and demand for CVD services have dramatically reduced across countries with potential for substantial, but avoidable, excess mortality during and after the COVID-19 pandemic.