key: cord-278313-gadui4r7 authors: Zakeri, R.; Bendayan, R.; Ashworth, M.; Bean, D. M.; Dodhia, H.; Durbaba, S.; O Gallagher, K.; Palmer, C.; Curcin, V.; Aitken, E.; Bernal, W.; Barker, R. D.; Norton, S.; Gulliford, M. C.; Teo, J. T.; Galloway, J.; Dobson, R. J.; Shah, A. M. title: A case-control and cohort study to determine the relationship between ethnic background and severe COVID-19 date: 2020-07-10 journal: nan DOI: 10.1101/2020.07.08.20148965 sha: doc_id: 278313 cord_uid: gadui4r7 Background. People of minority ethnic background may be disproportionately affected by severe COVID-19 for reasons that are unclear. We sought to examine the relationship between ethnic background and (1) hospital admission for severe COVID-19; (2) in-hospital mortality. Methods. We conducted a case-control study of 872 inner city adult residents admitted to hospital with confirmed COVID-19 (cases) and 3,488 matched controls randomly sampled from a primary healthcare database comprising 344,083 people resident in the same region. To examine in-hospital mortality, we conducted a cohort study of 1827 adults consecutively admitted with COVID-19. Data collected included hospital admission for COVID-19, demographics, comorbidities, in-hospital mortality. The primary exposure variable was self-defined ethnicity. Results. The 872 cases comprised 48.1% Black, 33.7% White, 12.6% Mixed/Other and 5.6% Asian patients. In conditional logistic regression analyses, Black and Mixed/Other ethnicity were associated with higher admission risk than white (OR 3.12 [95% CI 2.63-3.71] and 2.97 [2.30- 3.85] respectively). Adjustment for comorbidities and deprivation modestly attenuated the association (OR 2.28 [1.87-2.79] for Black, 2.66 [2.01-3.52] for Mixed/Other). Asian ethnicity was not associated with higher admission risk (OR 1.20 [0.86-1.66]). In the cohort study of 1827 patients, 455 (28.9%) died over a median (IQR) of 8 (4-16) days. Age and male sex, but not Black (adjusted HR 0.84 [0.63-1.11]) or Mixed/Other ethnicity (adjusted HR 0.69 [0.43-1.10]), were associated with in-hospital mortality. Asian ethnicity was associated with higher in-hospital mortality (adjusted HR 1.54 [0.98-2.41]). Conclusions. Black and Mixed ethnicity are independently associated with greater admission risk with COVID-19 and may be risk factors for development of severe disease. Comorbidities and socioeconomic factors only partly account for this and additional ethnicity-related factors may play a large role. The impact of COVID-19 may be different in Asians. SARS-CoV2 (Severe acute respiratory syndrome coronavirus 2) is a highly transmissible respiratory pathogen that usually causes minor illness but in a small proportion of individuals leads to severe systemic disease (Coronavirus disease 2019 ) with 1-2% mortality. [1] [2] [3] [4] Older people, males, and those with comorbidities such as diabetes and cardiovascular disorders are over-represented among those requiring hospital admission. [1] [2] [3] [4] As COVID-19 spread to multiethnic populations in Western Europe and North America, numerous reports suggested that individuals of Black, Asian or Minority Ethnic background suffer a higher burden of severe disease. [5] [6] [7] [8] [9] Audit data on patients admitted to UK intensive care units (ICU) with COVID-19 observed a substantially higher proportion of minority background patients than in previous years. 7 US data reported higher per-capita mortality rates for African-American and Latino versus White people in several cities, though the reasons for these differences are unclear. 6 A UK Office of National Statistics (ONS) analysis suggested that individuals of Black and South Asian descent had a higher likelihood of death than White people after adjustment for demographic and socioeconomic factors, but was limited by lack of information on comorbidities and the use of historic (2011) data for the reference population. 8 The relative importance of contextual demographic and socioeconomic conditions and the prevalence of long-term morbidities remains unclear. These reports also do not distinguish between ethnicity-related differences in likelihood of infection, severity of initial disease or in-hospital survival. COVID-19 mortality rates vary substantially by region and within urban conurbations. Nine of the ten UK local authorities with the highest age-standardised mortality rates are inner London Boroughs with high proportions of minority ethnic background people, socioeconomic deprivation and health inequalities. 10 It is essential to dissect the complex relationship among ethnicity, socioeconomic factors and comorbidities to properly inform public health policy and mitigate the impact of COVID-19 on minority communities. We undertook an analysis of the relationship between ethnicity and severe COVID-19 in an inner London region with approximately 40% Black and minority ethnic background people among a population of 1.26 million. Our aims were to (1) determine the relationship between ethnicity, local population demography, socioeconomic profiles, individual-level comorbidities, and hospital admission for . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 10, 2020. . https://doi.org/10.1101/2020.07.08.20148965 doi: medRxiv preprint severe COVID-19; (2) establish whether ethnicity is associated with in-hospital outcome of severe COVID-19. We conducted an observational cohort study at King's College Hospital Foundation Trust (KCHFT), which comprises two separate hospital sites in south London. We included consecutive adult patients (age ≥18 years) requiring emergency hospital admission with laboratory-confirmed COVID-19, between 1 March and 2 June, 2020. Eligible patients tested positive for viral RNA by quantitative RT-PCR in nasopharyngeal and oropharyngeal swabs ( Figure 1 ). We performed a matched case-control (case-population) study with the subset of admitted patients who were inner city residents (>55% of the total hospital cohort). We matched COVID-19 patients (i.e. cases) with population controls sampled from the same inner city region using data from a primary healthcare database (Lambeth DataNet). 11,12 This dataset includes deidentified data on 344,083 (96.8%) community-resident adults registered with 41 practices in inner south-east London. 13 For each case, we randomly sampled four controls who were individually matched to cases by age (within 5-year age bands) and sex. Demographic and clinical data for admitted patients were retrieved from the electronic health record (EHR). We used well-validated natural language processing (NLP) informatics tools belonging to the CogStack ecosystem to access both structured fields and unstructured text in the EHR. [14] [15] [16] Additional manual extraction, clinician case review, and mandatory hospital datasets were used for missing variables and validation. Individual-level anonymised primary care EHR data for the case-population study were extracted from the primary care database. The primary exposure variable was self-reported ethnicity according to the 18 categories recommended by the ONS. 17 These were reduced into four groups: White (British, Irish, Gypsy, . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 10, 2020. Comorbidities were categorised as present if recorded at any time in the EHR up to and including the day of admission (or data extraction in primary care). Multimorbidity was defined as 2 or more comorbidities. 19 Socioeconomic status was estimated using the English Indices of Multiple Deprivation (IMD) score, presented in quintiles, as derived from residential postcode and Low Super Output Area (2019) codes. 20 The same variable definitions were used across primary and secondary care, using internationally recognised terminology. Outcomes included hospital admission for COVID-19 and in-hospital mortality. The secondary outcome of ICU admission was evaluated in the hospital cohort. Start of follow-up for all analyses was taken as the date of self-reported symptom onset. Where symptom onset was unavailable (27%) and admission date was within 4 days of a COVID-positive RT-PCR test, admission date was used. This was based on the median duration between symptom onset and admission date for patients presenting with COVID-19. Outcomes were ascertained to 2 June 2020. Patient characteristics were summarised using frequency (%) and median with interquartile range (IQR). Comparisons across ethnic groups were made using the χ2 or Fisher's exact test for categorical variables and 1-way ANOVA or Kruskal-Wallis test for continuous variables. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 10, 2020. . https://doi.org/10.1101/2020.07.08.20148965 doi: medRxiv preprint Conditional logistic regression models were fitted to evaluate the association between ethnicity and hospital admission for COVID-19. All models were adjusted for the matching variables (age and sex) to eliminate residual confounding 21 and based on their previously reported associations with COVID-19. Fully adjusted models included other potential confounding variables: IMD quintile and comorbidities as outlined above. White ethnicity was used as the reference group. In the hospital cohort, we evaluated the association between ethnicity and risk of in-hospital death in a time-to-event framework using competing risks regression, based on Fine and Gray's proportional sub-hazards model, 22 with discharge from hospital assigned as a competing risk. For the secondary outcome of ICU admission, competing risks were death or discharge from hospital. Patient records were censored at the earlier of discharge from hospital or study end date. Univariable, age-and sex-adjusted, and fully adjusted multivariable models were performed as for the case-population study with White ethnicity as the reference group. Analyses were performed using STATA/IC (v16.1; StataCorp LLC, TX). Since BMI was missing in >30% of hospitalised patients, the primary analyses were performed without adjustment for BMI. To explore confounding by BMI, all analyses were repeated in the subset of patients with BMI data available. Additional sensitivity analyses were performed with imputation of missing variables using multiple imputations by chain equations. 23 This study adhered to the principles of the UK Data Protection Act 2018 and UK National Health . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 10, 2020. . https://doi.org/10.1101/2020.07.08.20148965 doi: medRxiv preprint The funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data and final responsibility for the decision to submit for publication. Between March 1 and June 2, 2020, 1,827 adults were admitted with laboratory-confirmed COVID-19. 872 patients were inner city residents and had ethnicity data available (Figure 1) . We compared this group with the population of adult residents registered with local primary care practices in the same region (n=286,950 Figure 1) . 65% of patients were from the lowest two quintiles of deprivation as compared to the 52% population prevalence. To identify whether ethnicity was independently associated with hospital admission, each patient (n=872 cases) was matched by age and sex to randomly sampled population controls (n=3,488, Figure 1 ). Cases were more likely than controls to be of Black or Mixed/Other ethnicity and from the lowest deprivation quintile (Supplemental Table 1 ). Cases also had a greater prevalence of each of 6 comorbidities, overall (Supplemental Table 1 ) and within ethnic groups (Supplemental Table 2 ). In analyses adjusting for the matching variables (age and sex) only, Black and Mixed/Other ethnicity were associated with higher odds of admission compared to White ethnicity ( is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 10, 2020. . https://doi.org/10.1101/2020.07.08.20148965 doi: medRxiv preprint between Black or Mixed/Other ethnicity and risk of admission, with the OR still 2·3 to 2·7-fold higher for these groups (Figure 2 ). There was no increase in admission risk associated with Asian ethnicity although the proportion of Asian patients in our study was small (5.6% cases, 7.8% controls). Similar results were obtained in analyses adjusting for BMI, in the subset of individuals with BMI data available. BMI accounted for a small proportion of ethnicityassociated admission risk (Supplemental Figure 2) . To assess whether the increased admission risk was specific for COVID-19, we examined all admissions from inner south-east London residents to the 5 main hospitals serving the region, both for COVID-19 (n=2434 patients; March 1 to June 2, 2020) and for respiratory infections over the preceding 12 months (n=7,081) (Supplemental Figure 3) . These data confirmed a disproportionately higher number of minority ethnic group patients among those admitted for COVID-19 compared with emergency respiratory infection admissions in the preceding year. Among 1,827 patients admitted to KCHFT with COVID-19 from either inner city or suburbs, 1,572 had ethnicity data available (Figure 1) White) ( Table 1) . Black patients had higher prevalence of hypertension, diabetes and CKD, and a higher proportion of overweight and obese individuals, than White patients. Conversely, Black patients had the lowest rates of CHD and COPD. Asian patients had higher prevalence of diabetes and obesity compared to White and a lower prevalence of COPD. 74% of Black patients resided in localities with the two lowest IMD quintiles as compared to 51% of Asian and 38% of White patients (p<0.001). Among patients with a recorded date for symptom onset (73.2%), the median duration of symptoms before admission was longer in minority ethnic groups (3 days for White, 4 days for Black, 7 days for Asian; p<0.001, Table 2 ) but did not vary substantially across IMD quintiles (median 4 days for both the most and least deprived quintiles). . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 10, 2020. . https://doi.org/10.1101/2020.07.08.20148965 doi: medRxiv preprint By 2 June 2020, 455 of 1,572 patients (28.9%) had died and 1,046 (66.5%) had been discharged. The median (IQR) length of stay was 8 (4-16) days. Age-adjusted cumulative incidence curves for in-hospital mortality by ethnic group are displayed in Figure 3A and stratified by sex in Supplemental Figure 4 . In unadjusted analyses, Black or Mixed/Other ethnicity were associated with a lower risk of death compared to White, however this was largely due to their younger age (Figure 4) . In ageand sex-adjusted and full adjusted analyses, the risk of death was not significantly different between Black or Mixed/Other ethnicity versus White. The adjusted analyses estimated increased mortality risk with Asian ethnicity but with considerable uncertainty; the number of Asian patients was relatively low in our cohort. Age-adjusted cumulative incidence curves for admission to ICU by ethnic group, and stratified by sex, are displayed in Figure 3B and Supplemental Figure 5 , respectively. Asian patients had significantly higher rates of ICU admission even after adjustment for age, sex, comorbidities and IMD ( Figure 5 ). Sensitivity analyses adjusting for BMI in the subset of patients with BMI available produced results similar to those in the main analysis (Supplemental Figure 6) . The association between ethnicity and in-hospital mortality remained consistent across age strata (Supplemental Figure 7) . Finally, using multiple imputation, fully adjusted hazard ratios for in-hospital mortality remained similar to the main analysis. People of Black and Minority ethnic background are reported to have a disproportionately high mortality from COVID-19 both in the US and the UK, but it is not established whether this is due to increased risk of infection, greater frequency of severe disease once infected, or worse inhospital survival. [5] [6] [7] [8] [9] The relative importance of socioeconomic deprivation, health inequalities or other underlying factors is also unclear. The impact of COVID-19 on minority communities is especially high in inner cities which have a multi-ethnic population. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 10, 2020. . https://doi.org/10.1101/2020.07.08.20148965 doi: medRxiv preprint The current study makes several new findings. First, we employ a case-population study to identify an approximately 3-fold higher risk of hospital admission with COVID-19 for Black and Mixed ethnicity groups (but not Asians) compared to White among inner city residents. After adjusting for comorbidities and socioeconomic deprivation score, there remains a 2·3 to 2·7-fold higher admission risk. Secondly, we find inter-ethnic variation in demographics and clinical characteristics in admitted patients. Minority ethnic group patients are on average 10-15 years younger than White patients yet have a higher prevalence of chronic medical conditions. Diabetes is especially prevalent in all non-white groups while Black patients also have high rates of hypertension and CKD. Thirdly, we find no association between Black or Mixed ethnicity and in-hospital outcome. An association with increased in-hospital mortality is found for Asian patients but should be cautiously interpreted due to the low numbers in this group. Overall, our results suggest that the higher COVID-19 mortality reported for Black people in prior studies is most likely driven by a higher rate of admissions rather than a worse in-hospital outcome. White people living in the same region may be driven either by an increased risk of infection or more aggressive early disease progression or both. An increased susceptibility to infection could occur for several reasons. It has been suggested that a higher prevalence of socioeconomic deprivation (e.g. with poor housing) and cultural factors such as living in multi-generational households may increase susceptibility in some ethnic groups. 5, 24 Adjustment for deprivation as assessed by IMD quintile had only a modest effect in our study. However, it is recognised that the complexity of disadvantage related to socioeconomic factors may be incompletely captured by the IMD score and that area-based metrics may not be directly comparable across ethnic groups. 25 Minority ethnic groups in the US and Europe have a higher prevalence of comorbidities such as diabetes compared with White groups, 26, 27 which may also increase susceptibility to infection and seemed to contribute to part of the additional admission risk. Beyond higher prevalence, the adverse effects of some long-term conditions are . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 10, 2020. . https://doi.org/10.1101/2020.07.08.20148965 doi: medRxiv preprint disproportionately amplified by specific ethnicity. For example, diabetes is more likely to progress to complications in African Americans, African Caribbeans and South Asians compared to White individuals. 27, 28 Similarly, the likelihood of incident cardiovascular disease in people with metabolic risk factors is amplified by African Caribbean and South Asian ethnicity. 29 Therefore, ethnicity-related amplification of comorbidity risks as well as latent socioeconomic factors could potentially contribute to part of the comorbidity-and deprivation-independent risk of admission for COVID-19. An alternative driver of higher admission risk in Black and Mixed ethnicity people may be that they are more likely once infected to progress to disease that requires admission (noting that an increase in mild infections per se should not affect admission risk or mortality). It is notable that the ethnicity distribution for admissions with respiratory infections in the same community population was markedly different from that for COVID-19 admissions, suggesting that more complex factors are involved with COVID-19. A higher prevalence of comorbidities such as diabetes could potentially contribute to more frequent progression to severe disease given that it enhances pro-inflammatory effects. Previous studies have shown that immune responses to pathogens are significantly different between individuals of African versus European ancestry, with African ancestry associated with a stronger inflammatory response. 30, 31 Our study was, however, not designed to distinguish between infection risk and more severe early disease as drivers of COVID-19 admission. We did not find evidence that in-hospital survival was significantly different between Black or Mixed ethnicity and White patients, suggesting that there are no major differences between these groups in life-threatening complications. The age-adjusted incidence of ICU admission was also similar for Black and White patients. A strong trend towards higher in-hospital death and admission to ICU was noted in Asians compared to the other groups. We also observed the longest symptom duration prior to admission in Asians, followed by Black and Mixed ethnicity patients, and then White patients. Whether this reflects difficulties in access to healthcare, differences in health-seeking behaviours, or is related to disease severity and progression requires further investigation. The in-hospital mortality data for Asians also need to be further . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 10, 2020. . https://doi.org/10.1101/2020.07.08.20148965 doi: medRxiv preprint explored in studies with larger numbers of Asian patients as well as for the different Asian subcategories. Most previous studies that addressed the relationship between ethnicity and COVID-19 either focused solely on mortality referenced to aggregate populations or made causal inferences from investigation of hospitalised patients. 7-9 The limitations of these approaches in terms of selection or collider bias have been discussed. 32 In our study, the use of a case-population design to assess the risk of admission for severe COVID-19 allowed us to compare the characteristics of admitted patients with a representative sample of the source population and thereby minimise selection bias. Our primary care database covered a large inner city region that closely matched the normal catchment area for the hospital. We cannot exclude the possibility that White patients are differentially admitted to other hospitals in the area but we consider this unlikely since emergency admissions are typically to the nearest hospital in the UK National Health Service. Moreover, we present data for all five hospitals in south-east London, which confirm excess COVID-19 admissions in the Black ethnic group. It is unlikely that our findings are explained by a lower threshold for admission in the Black group, because symptom duration was generally greater for these patients. This approach strengthens our conclusions regarding the interrelationship between ethnicity, comorbidities, deprivation score and risk of admission. Although our study was performed in a single region of inner London, the results are likely to be applicable to similar large cities with multi-ethnic populations around the world, especially in relation to the risks that appear independent of socioeconomic deprivation. Our study has some limitations. In common with the majority of published studies so far, we did not have data on the exposure to SARS-CoV2 in the community nor on the prevalence of asymptomatic or mild disease. Our conclusions regarding risk of infection are therefore limited by this unknown factor. A small proportion of our cohort had missing ethnicity, consistent with other similar studies. While information on deprivation related to locality was available, we did not have individual-level data on factors such as number of people in the household, occupation, availability of personal protective material, and adherence to social distancing measures. Detailed information on the degree of control of comorbidities was also unavailable. Conclusions regarding obesity were limited by the high proportion of patients of all ethnic groups for whom . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 10, 2020. . https://doi.org/10.1101/2020.07.08.20148965 doi: medRxiv preprint data on BMI was unavailable. However, we performed sensitivity analyses to exclude the possibility that our conclusions were confounded by these missing data. Finally, the ethnic categories used in our study were broad categories and the analyses do not explore variation within an ethnic category, e.g. Black African versus Black Caribbean. Despite these limitations, the current findings on risk of admission related to Black and Mixed ethnicity and the lack of association with in-hospital outcome for the individual patient provide new insights to inform public health measures to tackle risk. Our results indicate that increased admission risk rather than worse in-hospital outcome may be the main contributor to higher mortality in Black patients. They also suggest that beyond the higher prevalence of comorbidities (especially diabetes and hypertension) and JTHT received research funding from Bristol-Myers-Squibb and iRhythm Technologies and holds shares <£5,000 in Glaxo Smithkline and Biogen. All other authors declare that they have no competing interests. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 10, 2020. . https://doi.org/10.1101/2020.07.08.20148965 doi: medRxiv preprint The authors declare that all data supporting the findings of this study are available within the article and its supplementary information files. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 10, 2020. . https://doi.org/10.1101/2020.07.08.20148965 doi: medRxiv preprint . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 10, 2020. . https://doi.org/10.1101/2020.07.08.20148965 doi: medRxiv preprint . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 10, 2020. . https://doi.org/10.1101/2020.07.08.20148965 doi: medRxiv preprint *Comorbidities include asthma, chronic obstructive pulmonary disease, coronary heart disease, hypertension, diabetes, chronic kidney disease. Hazard ratios are compared to White ethnicity Model 1adjusted for age and sex is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 10, 2020. . https://doi.org/10.1101/2020.07.08.20148965 doi: medRxiv preprint hypertension, diabetes, chronic kidney disease. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 10, 2020. . Table 1 Characteristics of cases and population controls 2 Supplemental Table 2 Characteristics of cases and age-sex-matched population controls stratified by ethnicity 3 Supplemental Figure 1 Age . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 10, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 10, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 10, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 10, 2020. . https://doi.org/10.1101/2020.07.08.20148965 doi: medRxiv preprint Hazard ratios are compared to White ethnicity Model 1adjusted for age and sex Model 2adjusted for age, sex and index of multiple deprivation (IMD) Model 3adjusted for age, sex and body mass index (BMI) Model 4adjusted for age, sex and comorbidities* Model 5 (fully adjusted model)adjusted for age, sex, IMD, BMI, and comorbidities *Comorbidities include asthma, chronic obstructive pulmonary disease, coronary heart disease, hypertension, diabetes, chronic kidney disease. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 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The English Indices of Deprivation Analysis of matched case-control studies A proportional hazards model for the subdistribution of a competing risk Multiple imputation of missing blood pressure covariates in survival analysis COVID-19 and African Americans Learning to live with complexity: Ethnicity, socioeconomic position, and health in Britain and the United States Incident type 2 diabetes mellitus in African American and White adults: The Atherosclerosis Risk in Communities Study Southall And Brent REvisited: Cohort profile of SABRE, a UK population-based comparison of cardiovascular disease and diabetes in people of European, Indian Asian and African Caribbean origins Disparities in diabetes-related multiple chronic conditions and mortality: The influence of race The relationship between metabolic risk factors and incident cardiovascular disease in Europeans, South Asians, and African Caribbeans: SABRE (Southall and Brent Revisited) -a prospective population-based study Genetic ancestry and natural selection drive population differences in immune responses to pathogens Impact of historic migrations and evolutionary processes on human immunity Collider bias undermines our understanding of COVID-19 disease risk and severity We are extremely grateful to all the clinicians and other staff who have looked after our patients.We thank the patient experts of the KERRI committee and Professor Clive Kay, Professor Alastair Baker, Professor Irene Higginson and Professor Jules Wendon for their support. We gratefully acknowledge the excellent help of Chris Fry, Dominic Thurgood and Isuf Ali in the