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Predictors of adverse outcome in the first and second waves of the COVID-19 pandemic: results from a UK centre

Predictors of adverse outcome in the first and second waves of the COVID-19 pandemic: results... Research Centre, Background/Aims: Data concerning differences in demographics/disease severity between Leicester, East Midlands, LE5 4PW, UK the first and second waves of COVID-19 are limited. We aimed to examine prognosis in Diabetes Research patients presenting to hospital with COVID-19 amongst different ethnic groups between the Centre, Leicester General Hospital, University of first and second waves in the UK. Leicester, Leicester, UK Methods: In this retrospective cohort study, we included 1763 patients presenting to a regional NIHR Applied Research Collaboration-East hospital centre in Leicester (UK) and compared those in the first (n = 956) and second (n = 807) Midlands, Leicester, UK waves. Admission National Early Warning Scores, mechanical ventilation and mortality rate kk22@le.ac.uk Manish Pareek were lower in the second wave compared with the first. Department of Respiratory Results: Thirty-day mortality risk in second wave patients was approximately half that of first Sciences, University of Leicester, Leicester, UK wave patients [adjusted hazard ratio (aHR) 0.55, 95% confidence interval (CI) 0.40–0.75]. In the Department of Infection second wave, Black patients were at higher risk of 30-day mortality than White patients (4.73, and HIV Medicine, University Hospitals of 1.56–14.3). Leicester NHS Trust, Conclusion: We found that disporportionately higher risks of death in patients from ethnic Leicester, UK Diabetes Research minority groups were not equivalent across consecutive waves of the pandemic. This suggests Centre, Leicester General that risk factors for death in those from ethnic minority groups are malleable and potentially Hospital, University of Leicester, Leicester, UK reversible. Our findings need urgent investigation in larger studies. NIHR Applied Research Collaboration-East Midlands, Leicester, UK Keywords: COVID-19, ethnicity, mortality, SARS-CoV-2 mp426@le.ac.uk Christopher A. Martin Daniel Pan Received: 27 August 2021; revised manuscript accepted: 31 December 2021. Department of Respiratory Sciences, University of Leicester, Leicester, UK Department of Infection Introduction high-exposure or frontline occupations and living and HIV Medicine, The COVID-19 pandemic has caused significant in larger, multigenerational households compared University Hospitals of Leicester NHS Trust, global morbidity and mortality and has dispro- with White groups. Limited evidence suggests Leicester, UK portionately affected ethnic minority groups. In that mortality in the second wave may be lower George Hills the absence of widespread immunity, many coun- than that of the first, but it is not known whether Department of Infection and HIV Medicine, tries will continue to suffer surges and/or waves of this is true for all ethnic groups. University Hospitals of infection, in association with the implementation Leicester NHS Trust, Leicester, UK and release of lockdown measures. Ethnic minor- To address this, we compared the demographics and Department of Clinical ity groups have been disproportionately affected clinical outcomes of patients with COVID-19 admit- Microbiology, University by COVID-19 – suffering increased infection, ted to a large UK centre serving a multiethnic popu- Hospitals of Leicester NHS Trust, Leicester, UK hospitalisation and death during the first wave of lation during the first and second waves of the current Deborah Modha the pandemic. Multiple reasons exist for this, but pandemic. We aimed to estimate the effect of ethnic- David R. Jenkins it is thought that the disproportionate risk is ity as an important determinant of mortality in both Department of Clinical Microbiology, University mainly attributable to a higher risk of infection, waves – and hypothesised that ethnicity was related Hospitals of Leicester NHS from living in deprived areas, working in to mortality in at least one wave of the pandemic. Trust, Leicester, UK journals.sagepub.com/home/tai 1 Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). Therapeutic Advances in Infectious Disease 9 Methods variables as count (%). Comparison was by Prashanth Patel Department of This retrospective cohort study was undertaken Wilcoxon rank-sum test for continuous variables Cardiovascular Sciences, at University Hospitals of Leicester (UHL) and chi-square test for categorical variables. University of Leicester, Leicester, UK National Health Service (NHS) Trust. This con- Department of Chemical sists of three hospitals: Leicester General Hospital, We established survival time in days by calculat- Pathology and Metabolic Diseases, University Glenfield Hospital and Leicester Royal Infirmary. ing the difference between date of positive swab Hospitals of Leicester NHS The trust provides all acute care to patients with and date of data extraction or death and used uni- Trust, Leicester, UK COVID-19 in the Leicestershire area [a popula- variable and multivariable Cox regression to Laura J. Gray Department of Health tion of just more than 1 million (2016 estimate)] establish factors associated with all-cause mortal- Sciences, University of within the United Kingdom, and provides sec- ity within 30 days of COVID-19 diagnosis. Leicester, Leicester, UK ondary, tertiary and intensive care. Care for acute Multiple imputation was used to replace missing Linda Barton Department of patients is provided by a government system data in all models fitted, and the multiple imputa- Haematology, University (NHS) and free for all patients irrespective of tion model included all variables for those being Hospitals of Leicester NHS Trust, Leicester, UK socioeconomic status at the point of use. Private imputed (for further details, see Supplementary William Jones care for those hospitalised with COVID-19 does information). To investigate the effects of multi- Business Intelligence not exist in our region– therefore, our data pro- ple imputation on the results, we conducted a Unit, University Hospitals of Leicester NHS Trust, vide complete coverage of the Leicestershire area. sensitivity analysis using only complete cases. All Leicester, UK Disease severity scoring for patients in the UK is analyses were conducted using Stata (StataCorp. Nigel J. Brunskill Department of guided by the National Early Warning Score 2019). Values of p < 0.05 were considered statis- Cardiovascular Sciences, (NEWS) 2 alert system. Outpatient care is also tically significant. University of Leicester, Leicester, UK provided by NHS hospitals. Department of Nephrology, Leicester General Hospital, We defined a case of COVID-19 as an adult Results Leicester, UK patient with a positive molecular test for SARS- Table 1 shows a description of the cohort strati- Pranab Haldar Department of Respiratory CoV-2 on nasopharyngeal swab. The first wave fied by pandemic wave. A total of 1763 patients Sciences, University of cohort included all cases between 1 March 2020 were included in the final analysis, 956 (54.2%) Leicester, Leicester, UK and data extraction on 28 April 2020. The sec- from the first wave and 807 (45.8%) from the sec- Department of Respiratory Medicine, University ond wave cohort included all cases between 1 ond wave. There were no significant demo- Hospitals of Leicester NHS September 2020 and data extraction on 15 graphic/comorbidity differences between the first Trust, Leicester, UK NIHR Leicester Biomedical November 2020. and second wave cohorts. Research Centre, Leicester, UK Testing for SARS-CoV-2 was based on the pres- Markers of COVID-19 severity and duration of *Joint first authors/ contributed equally ence of symptoms during the first wave; however, hospital stay stratified by ethnicity are shown in †Joint senior authors a nationwide policy of universal testing for all Table 2. Admission NEWS was significantly emergency admissions was adopted from 27 April higher in the first wave compared with the second 2020. Molecular testing was made available for wave in those of White and South Asian ethnicity. hospitalised patients since the start of the first The proportion of deaths after diagnosis with cases of COVID-19 in the United Kingdom, on COVID-19 was higher in the first wave than in 27 January 2020. the second wave. This was observed in White and South Asian patients (26.1% versus 10.8%, We extracted data from the electronic hospital p < 0.001 and 22.1% versus 10.9%, p = 0.001, record concerning age, sex, self-reported ethnic- respectively) but not in Black patients (16.3% ity, postcode, comorbidities (see Supplemental versus 15.4%, p = 0.922). When those who died Table 1), NEWS 2 on admission, admission/dis- and those who were not discharged at the point of charge date, highest supplementary oxygen flow data extraction are excluded, median duration of rate during admission, dexamethasone use, hospital stay was longer in the second wave com- requirement for mechanical ventilation and date pared with the first (4 days, IQR 1–10 versus of death. Ethnicity was categorised as White, 5 days IQR 1–9.5, p = 0.016). South Asian, Black and Other (see Supplemental Table 2). Postcode was used to derive Index of Supplemental Table 3 shows Cox regression for Multiple Deprivation (IMD). survival to 30 days in the whole cohort. Follow-up time (time in days between diagnosis and death Continuous variables were summarised as median or data extraction) was significantly shorter in the [interquartile range (IQR)] and categorical second wave compared with the first [14 (6–26) 2 journals.sagepub.com/home/tai CA Martin, D Pan et al. Table 1. Demographic and comorbidity characteristics of first and second wave patients. Variable Total First Wave Second Wave n = 1763 (1 March–28 April) (1 September–15 November) n = 956 n = 807 Age in years, med (IQR) 66 (52–79) 66 (52–78.5) 67 (51–79) Sex, n (%) Female 759 (43.0) 425 (44.5) 334 (41.4) Male 1004 (57.0) 531 (55.5) 473 (58.6) Ethnicity, n (%) White 1096 (62.2) 605 (63.3) 491 (60.8) South Asian 501 (28.4) 263 (27.5) 238 (29.5) Black 69 (3.9) 43 (4.5) 26 (3.2) Other 97 (5.5) 45 (4.7) 52 (6.5) IMD Quintile, n (%) 1 (most deprived) 329 (18.6) 186 (19.5) 143 (17.7) 2 444 (25.2) 254 (26.6) 190 (23.5) 3 308 (17.5) 150 (15.7) 158 (19.6) 4 355 (20.1) 187 (19.6) 168 (20.8) 5 (least deprived) 314 (17.8) 178 (18.5) 136 (16.9) Missing 13 (0.8) 1 (0.1) 12 (1.5) Comorbidity type, n (%) Hypertension 525 (29.8) 277 (29.0) 248 (30.7) Other cardiovascular 194 (11.0) 94 (9.8) 100 (12.4) Cerebrovascular 93 (5.3) 52 (5.4) 41 (5.1) Respiratory 242 (13.7) 133 (13.9) 109 (13.5) Diabetes 301 (17.0) 167 (17.5) 134 (16.6) Comorbidity number, n (%) 0 881 (50.0) 466 (48.7) 415 (51.4) 1 325 (18.4) 193 (20.2) 132 (16.4) 557 (31.6) 297 (31.1) 260 (32.2) ⩾2 IMD, Index of Multiple Deprivation; IQR, interquartile range; med, median versus 19 (9–27), p < 0.0001]. Factors associated first wave, those in the second wave had around with increased risk of 30-day mortality were half the risk of 30-day mortality [adjusted hazard increasing age, male sex, presence of diabetes and ratio (aHR) 0.55, 95% confidence interval (CI) admission NEWS. Compared with those in the 0.40–0.75]. journals.sagepub.com/home/tai 3 Therapeutic Advances in Infectious Disease 9 4 journals.sagepub.com/home/tai Table 2. Markers of severity and outcome data stratified by pandemic wave and ethnicity. Variable First wave (n = 956) Second wave (n = 807) Total White South Asian Black Other Total White South Asian Black Other NEWS score on 3 (1–4.5)* 2 (1–4)* 3 (1–5)* 3 (1–5) 3 (1–7) 2 (0–4)* 2 (0–4)* 2 (0–4)* 2 (0–4) 0 (1–4) admission, med (IQR) Missing, n (%) 20 (2.1) 10 (1.7) 5 (1.9) 1 (2.3) 4 (8.9) 4 (0.5) 1 (0.2) 3 (1.3) 0 (0.0) 0 (0.0) Maximum oxygen flow rate 2 (0–10)* 3 (0–10)* 2 (0–8) 2 (0–4) 0 (0–4) 2 (0–6)* 2 (0–6)* 2 (0–8) 0 (0–8) 0 (0–3) received during admission (L/min), med (IQR) Not mechanically ventilated 886 (92.7%) 571 (94.4%) 238 (90.5%) 39 (90.7%) 38 (84.4%) 778 (96.5%) 479 (97.6%) 226 (95.0%) 25 (96.2%) 48 (92.3%) † † † † Mechanically ventilated 70 (7.3%) 34 (5.6%) 25 (9.5%) 4 (9.3%) 7 (15.6%) 29 (3.6%) 12 (2.4%) 12 (5.0%) 1 (3.9%) 4 (7.7%) Survived 728 (76.2%) 447 (73.9%) 205 (78.0%) 36 (83.7%) 40 (88.9%) 723 (89.6%) 438 (89.2%) 212 (89.1%) 22 (84.6%) 51 (98.1%) † † † † † † Died 228 (23.9%) 158 (26.1%) 58 (22.1%) 7 (16.3%) 5 (11.1%) 84 (10.4%) 53 (10.8%) 26 (10.9%) 4 (15.4%) 1 (1.9%) Excluding those not discharged or dead at the time of data extraction Variable First wave (n = 794) Second wave (n = 576) Not mechanically ventilated 760 (95.7%) 482 (96.2%) 211 (95.1%) 36 (94.7%) 31 (93.9%) 562 (97.6%) 332 (97.9%) 180 (97.3%) 19 (95.0%) 31 (96.9%) Mechanically ventilated 34 (4.3%) 19 (3.8%) 11 (5.0%) 2 (5.3%) 2 (6.1%) 14 (2.4%) 7 (2.1%) 5 (2.7%) 1 (5.0%) 1 (3.1%) Survived 566 (71.3%) 343 (68.5%) 164 (73.9%) 31 (81.6%) 28 (84.9%) 492 (85.4%) 286 (84.4%) 159 (86.0%) 16 (80.0%) 31 (96.9%) † † † † † † Died 228 (28.7%) 158 (31.5%) 58 (26.1%) 7 (18.4%) 5 (15.2%) 84 (14.6%) 53 (15.6%) 26 (14.1%) 4 (20.0%) 1 (3.1%) Excluding those who died and those not discharged at the time of data extraction Variable First wave (n = 566) Second wave (n = 492) Length of stay in days, med 4 (1–10)* 5 (1–10) 3 (1–6)* 3 (0–7) 1 (0–6.5) 5 (1–9.5)* 5 (2–11) 4 (1–9)* 4 (1.5–6) 3 (1–8) (IQR) IQR, interquartile range; NEWS, National Early Warning Score. Wilcoxon rank-sum p < 0.05. Chi-square p < 0.05. CA Martin, D Pan et al. Table 3. Multivariable Cox proportional hazards model for survival at 30 days from COVID-19 diagnosis in the first and second pandemic waves. Variable First wave (n = 956) Second wave (n = 807) aHR (95% CI) p value aHR (95% CI) p value Age 1.05 (1.04–1.07) 1.07 (1.05–1.10) <0.001 <0.001 Sex Female Ref – Ref – Male 1.50 (1.13–2.00) 0.005 2.22 (1.29–3.81) 0.004 Ethnicity White Ref – Ref – South Asian 1.04 (0.73–1.49) 0.81 0.90 (0.52–1.56) 0.71 Black 0.88 (0.38–2.04) 0.76 4.73 (1.56–14.34) 0.006 Other 0.71 (0.28–1.78) 0.47 0.50 (0.06–3.87) 0.51 IMD quintile 1 (least deprived) Ref – Ref – 2 1.20 (0.79–1.83) 0.38 1.00 (0.49–2.05) 1.00 3 0.98 (0.60–1.59) 0.92 1.20 (0.58–2.49) 0.62 4 0.89 (0.56–1.41) 0.62 0.59 (0.27–1.29) 0.19 5 (most deprived) 0.91 (0.58–1.43) 0.69 0.81 (0.35–1.86) 0.62 Comorbidity type Hypertension 0.84 (0.58–1.22) 0.36 1.96 (1.00–3.82) 0.05 Other cardiovascular 0.78 (0.51–1.21) 0.26 1.19 (0.68–2.09) 0.55 Stroke 1.25 (0.76–2.06) 0.38 0.71 (0.24–2.06) 0.53 Respiratory disease 1.00 (0.64–1.58) 0.99 1.49 (0.80–2.75) 0.21 Diabetes 1.43 (0.99–2.08) 0.06 2.03 (1.15–3.59) 0.02 Comorbidity number 0 Ref – Ref – 1 1.46 (0.95–2.24) 0.08 2.58 (1.07–6.23) 0.03 1.24 (0.69–2.22) 0.48 1.23 (0.41–3.68) 0.71 ⩾2 NEWS score on admission 1.28 (1.23–1.33) 1.34 (1.23–1.46) <0.001 <0.001 Treatment Did not receive dexamethasone Ref – Ref – Received dexamethasone 0.14 (0.02–0.99) 0.049 1.55 (0.95–2.51) 0.08 aHR, adjusted hazard ratio; CI, confidence interval; IMD, Index of Multiple Deprivation; NEWS, National Early Warning Score. journals.sagepub.com/home/tai 5 Therapeutic Advances in Infectious Disease 9 Separate multivariable Cox regression models for COVID-19 (dexamethasone). Clearly a reduc- survival to 30 days in the first and second waves tion in COVID-19 mortality is desirable; however, are shown in Table 3. In both pandemic waves, increased survival from COVID-19 in the second age, male sex and admission NEWS score are sig- wave may contribute to pressure on the healthcare nificantly associated with a higher risk of 30-day system through increased hospital bed occupancy. mortality. In the first wave, ethnicity did not Indeed our analysis shows that duration of hospital impact upon 30-day mortality; however, in the stay was longer in the second wave than in the first. second wave, as compared with White patients, Nonpharmaceutical interventions, such as man- those of Black ethnicity were almost 5 times as dating masks and lockdowns, may have also likely to die within 30 days (aHR 4.72, 95% CI affected the number of patients infected and thus 1.56–14.43). When the waves were analysed in turn, the number of deaths, but the city of together, the interaction between pandemic wave Leicester remained in full lockdown (stay-at-home and Black ethnicity was not significant (aHR except for essential purchases, remote work, one 3.25, 95% CI 0.87–12.07, p = 0.079). When only exercise per day, cancellation of public gatherings complete cases are analysed, significant findings and social events and no travel abroad) from 23 do not change. March 2020 to the end of the study. We observed that mortality rates in the second Discussion pandemic wave may not be equal across ethnic In this study within an ethnically diverse cohort, groups. In our second wave cohort, those of Black we found a number of novel observations. First, ethnicity were at higher risk of 30-day mortality there were no significant differences in demo- than White individuals (an effect not seen in the graphic characteristics between patients in the first wave cohort). These findings are opposite to first and second COVID-19 pandemic waves and a national UK study by Mathur et al., which that markers of severe COVID-19 (NEWS, found that compared with the first wave, risks for requirement for mechanical ventilation and death were attenuated for those from Black ethnic COVID-19-associated mortality) were lower in groups compared with White ethnic groups. the second wave of the pandemic compared with Ethnicity is a social determinant of health. the first. We found those of Black ethnicity were Therefore, it is likely that the differences in risks at higher risk of 30-day mortality than those of seen in our study reflect the number of infections White ethnicity in the second wave. that occurred in the local community and conse- quently, the number of people who present to COVID-19 case fatality rates have fallen in the sec- hospital. A disproportionate number of infections ond wave in 43 of the 53 countries with the highest in those of Black ethnicity may reflect socioeco- total COVID-19-related deaths. Foremost among nomic factors, such as occupation, which prevent the explanations for this observation is the them from being able to stay at home on a second increased availability of testing in the second wave lockdown. Leicester is an especially ethnically which may increase the number of paucisympto- diverse part of the United Kingdom, where matic/asymptomatic individuals who are tested. In approximately 30% of the population is born out- our study, the change in SARS-CoV-2 testing pol- side of Europe and North America, and individu- icy from symptom-driven testing to universal test- als from the Indian subcontinent alone make up ing would be expected to pick up bystander 15% of the population. However, we recom- infection in those attending hospital for reasons mend cautious interpretation of this finding, due unrelated to COVID-19 and could generate bias to the small number of Black patients in our sam- towards a less symptomatic sample in the second ple and the nonsignificant interaction between wave. Other suggested mechanisms include the ethnicity and pandemic wave. Nonetheless, given changing demographic profile of COVID-19 cases the higher risk of SARS-CoV-2 infection and over time, with a smaller proportion of elderly adverse outcome from COVID-19 in ethnic patients (a group at high risk of adverse outcome minority groups compared with those of White 1,5,13,14 from COVID-19) in the second pandemic wave ethnicity, this observation requires urgent 8,9 with more people shielding, although we found follow-up by larger studies with a greater propor- no such differences in our dataset. In addition, in tion of Black participants and emphasises the contrast to the early part of the first wave, there is importance of targeted interventions such as tai- an effective pharmacological treatment for severe lored public health messaging in these groups. 6 journals.sagepub.com/home/tai CA Martin, D Pan et al. This study has limitations. Data are from a single Methodology; Project administration; Writing – centre and our data do not allow us to control for original draft; Writing – review & editing the change in testing policy between waves. We Daniel Pan: Conceptualisation; Data curation; found a shorter median follow-up time in second Formal analysis; Investigation; Methodology; wave patients. This is likely due to the fact that Writing – original draft; Writing – review & editing while the first wave data capture peak COVID-19 hospital admissions in early April with data George Hills: Conceptualisation; Data curation; extraction a month later, COVID-19 admissions Investigation; Writing – review & editing continued to increase after data extraction for the Deborah Modha: Conceptualisation; Writing – second wave, meaning that a greater proportion review & editing of the second wave cohort were admitted close to the point of data extraction when compared with Prashanth Patel: Conceptualisation; Data cura- the first wave. However, we conducted an explor- tion; Investigation; Writing – review & editing atory analysis excluding those admitted after the Laura Gray: Data curation; Formal analysis; first wave ‘peak’ and still demonstrated a signifi- Writing – review & editing cant protective effect of being admitted during the second wave compared with the first. Mortality David Jenkins: Conceptualisation; Writing – rates and severity of COVID-19 may change in review & editing subsequent waves of the pandemic, particularly Linda Barton: Investigation; Writing – review & with the emergence of the new variants of SARS- editing CoV-2. We only collected routinely available var- iables that were recorded within our clinical William Jones: Data curation; Writing – review systems and as such, we do not have granular & editing information – such as occupation – which may Nigel Brunskill: Methodology; Writing – review have affected their risk of infection. No patients in & editing the United Kingdom were vaccinated at the time of study as part of national policy – these findings Pranab Haldar: Conceptualisation; Writing – may be different now that a significant proportion review & editing of the UK population has been vaccinated. The Kamlesh Khunti: Conceptualisation; Writing – emergence of novel variants, especially during the review & editing period of the second wave, may have had an impact on our study outcomes – however, Manish Pareek: Conceptualisation; Formal genomic sequencing is not routinely performed in analysis; Methodology; Project administration; all our hospitals. Despite these limitations, our Supervision; Writing – review & editing findings, particularly those related to ethnicity, must be investigated by larger studies to ensure that the inequalities of the first wave are not Conflict of interest statement allowed to widen. The authors declared the following potential con- flicts of interest with respect to the research, Acknowledgements authorship and/or publication of this article: KK CAM and DP are National Institute for Health is the Director of the University of Leicester Research (NIHR) academic clinical fellows. LG Centre for Black Minority Ethnic Health, Trustee and KK are supported by NIHR Applied Research of the South Asian Health Foundation and Chair Collaboration East Midlands (ARC EM). KK of the Ethnicity Subgroup of SAGE. MP reports and MP are supported by the NIHR Leicester grants and personal fees from Gilead Sciences Biomedical Research Centre (BRC). The views and personal fees from QIAGEN, outside the expressed are those of the author(s) and not nec- submitted work. essarily those of the NIHR, National Health Service (NHS) or the Department of Health and Funding Social Care. The authors disclosed receipt of the following financial support for the research, authorship Author contributions and/or publication of this article: This work was Christopher Martin: Conceptualisation; Data supported by the National Institute for Health curation; Formal analysis; Investigation; Research (NIHR). The funders had no role in journals.sagepub.com/home/tai 7 Therapeutic Advances in Infectious Disease 9 ethnicity and household size: results from an design, data collection and analysis, decision to observational cohort study. EClinicalMedicine publish or preparation of the article. MP is sup- 2020; 25: 100466. ported by an NIHR Development and Skills Enhancement Award and funding from UKRI/ 6. Thomas R. All hospital emergency patients to MRC (MR/V027549/1). be tested for coronavirus, https://www.hsj.co.uk/ coronavirus/all-hospital-emergency-patients-to- be-tested-for-coronavirus/7027526.article (2020, Ethics statement accessed 6 January 2021). Ethical approval was not required after consulta- tion with the National Health Service (NHS) 7. Royal College of Physicians. National Early Health Research Authority decision aid and the Warning Score (NEWS) 2: Standardising the Caldicott Guardian and the study was approved assessment of acute-illness severity in the NHS. as a service evaluation/audit (UHL10579). Updated report of a working party. London: RCP, ORCID iD 8. Burgess S, Smith D, Kenyon JC, et al. Lightening George Hills https://orcid.org/0000-0003- the viral load to lessen covid-19 severity. BMJ 0172-6156 2020; 371: m4763. 9. Venkatesan P. The changing demographics Supplemental material of COVID-19. Lancet Respir Med 2020; 8: Supplemental material for this article is available e95. online. 10. Dexamethasone in Hospitalized Patients with Covid-19— preliminary report. N Engl J Med 2021; 384: 693–704. References 11. Mathur R, Rentsch CT, Morton CE, et al. 1. Sze S, Pan D, Nevill CR, et al. Ethnicity and Ethnic differences in SARS-CoV-2 infection and clinical outcomes in COVID-19: a systematic COVID-19-related hospitalisation, intensive care review and meta-analysis. EClinicalMedicine 2020; unit admission, and death in 17 million adults 29: 100630. in England: an observational cohort study using the OpenSAFELY platform. Lancet 2021; 397: 2. Ferguson NM, Laydon D, Nedjati-Gilan G, 1711–1724. et al. Report 9: impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 12. Pareek M, Eborall HC, Wobi F, et al. mortality and healthcare demand, https://www. Community-based testing of migrants for imperial.ac.uk/media/imperial-college/medicine/ infectious diseases (COMBAT-ID): impact, sph/ide/gida-fellowships/Imperial-College- acceptability and cost-effectiveness of identifying COVID19-NPI-modelling-16-03-2020.pdf infectious diseases among migrants in primary (2020, accessed 21 December 2020). care: protocol for an interrupted time-series, qualitative and health economic analysis. BMJ 3. Pan D, Martin CA, Nazareth J, et al. Ethnic Open 2019; 9: e029188. disparities in COVID-19: increased risk of infection or severe disease? Lancet 2021; 398: 13. Martin CA, Patel P, Goss C, et al. Demographic 389–390. and occupational determinants of anti-SARS- CoV-2 IgG seropositivity in hospital staff. J Public 4. Fan G, Yang Z, Lin Q, et al. Decreased case Health. Epub ahead of print 16 November 2020. fatality rate of COVID-19 in the second wave: DOI: 10.1093/pubmed/fdaa199. a study in 53 countries or regions. Transbound Emerg Dis 2020; 68: 213–215. 14. Williamson EJ, Walker AJ, Bhaskaran K, et al. Visit SAGE journals online journals.sagepub.com/ 5. Martin CA, Jenkins DR, Minhas JS, et al. Socio- OpenSAFELY: factors associated with COVID-19 home/tai demographic heterogeneity in the prevalence of death in 17 million patients. Nature 2020; 584: SAGE journals COVID-19 during lockdown is associated with 430–436. 8 journals.sagepub.com/home/tai http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Therapeutic Advances in Infectious Disease SAGE

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Abstract

Research Centre, Background/Aims: Data concerning differences in demographics/disease severity between Leicester, East Midlands, LE5 4PW, UK the first and second waves of COVID-19 are limited. We aimed to examine prognosis in Diabetes Research patients presenting to hospital with COVID-19 amongst different ethnic groups between the Centre, Leicester General Hospital, University of first and second waves in the UK. Leicester, Leicester, UK Methods: In this retrospective cohort study, we included 1763 patients presenting to a regional NIHR Applied Research Collaboration-East hospital centre in Leicester (UK) and compared those in the first (n = 956) and second (n = 807) Midlands, Leicester, UK waves. Admission National Early Warning Scores, mechanical ventilation and mortality rate kk22@le.ac.uk Manish Pareek were lower in the second wave compared with the first. Department of Respiratory Results: Thirty-day mortality risk in second wave patients was approximately half that of first Sciences, University of Leicester, Leicester, UK wave patients [adjusted hazard ratio (aHR) 0.55, 95% confidence interval (CI) 0.40–0.75]. In the Department of Infection second wave, Black patients were at higher risk of 30-day mortality than White patients (4.73, and HIV Medicine, University Hospitals of 1.56–14.3). Leicester NHS Trust, Conclusion: We found that disporportionately higher risks of death in patients from ethnic Leicester, UK Diabetes Research minority groups were not equivalent across consecutive waves of the pandemic. This suggests Centre, Leicester General that risk factors for death in those from ethnic minority groups are malleable and potentially Hospital, University of Leicester, Leicester, UK reversible. Our findings need urgent investigation in larger studies. NIHR Applied Research Collaboration-East Midlands, Leicester, UK Keywords: COVID-19, ethnicity, mortality, SARS-CoV-2 mp426@le.ac.uk Christopher A. Martin Daniel Pan Received: 27 August 2021; revised manuscript accepted: 31 December 2021. Department of Respiratory Sciences, University of Leicester, Leicester, UK Department of Infection Introduction high-exposure or frontline occupations and living and HIV Medicine, The COVID-19 pandemic has caused significant in larger, multigenerational households compared University Hospitals of Leicester NHS Trust, global morbidity and mortality and has dispro- with White groups. Limited evidence suggests Leicester, UK portionately affected ethnic minority groups. In that mortality in the second wave may be lower George Hills the absence of widespread immunity, many coun- than that of the first, but it is not known whether Department of Infection and HIV Medicine, tries will continue to suffer surges and/or waves of this is true for all ethnic groups. University Hospitals of infection, in association with the implementation Leicester NHS Trust, Leicester, UK and release of lockdown measures. Ethnic minor- To address this, we compared the demographics and Department of Clinical ity groups have been disproportionately affected clinical outcomes of patients with COVID-19 admit- Microbiology, University by COVID-19 – suffering increased infection, ted to a large UK centre serving a multiethnic popu- Hospitals of Leicester NHS Trust, Leicester, UK hospitalisation and death during the first wave of lation during the first and second waves of the current Deborah Modha the pandemic. Multiple reasons exist for this, but pandemic. We aimed to estimate the effect of ethnic- David R. Jenkins it is thought that the disproportionate risk is ity as an important determinant of mortality in both Department of Clinical Microbiology, University mainly attributable to a higher risk of infection, waves – and hypothesised that ethnicity was related Hospitals of Leicester NHS from living in deprived areas, working in to mortality in at least one wave of the pandemic. Trust, Leicester, UK journals.sagepub.com/home/tai 1 Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). Therapeutic Advances in Infectious Disease 9 Methods variables as count (%). Comparison was by Prashanth Patel Department of This retrospective cohort study was undertaken Wilcoxon rank-sum test for continuous variables Cardiovascular Sciences, at University Hospitals of Leicester (UHL) and chi-square test for categorical variables. University of Leicester, Leicester, UK National Health Service (NHS) Trust. This con- Department of Chemical sists of three hospitals: Leicester General Hospital, We established survival time in days by calculat- Pathology and Metabolic Diseases, University Glenfield Hospital and Leicester Royal Infirmary. ing the difference between date of positive swab Hospitals of Leicester NHS The trust provides all acute care to patients with and date of data extraction or death and used uni- Trust, Leicester, UK COVID-19 in the Leicestershire area [a popula- variable and multivariable Cox regression to Laura J. Gray Department of Health tion of just more than 1 million (2016 estimate)] establish factors associated with all-cause mortal- Sciences, University of within the United Kingdom, and provides sec- ity within 30 days of COVID-19 diagnosis. Leicester, Leicester, UK ondary, tertiary and intensive care. Care for acute Multiple imputation was used to replace missing Linda Barton Department of patients is provided by a government system data in all models fitted, and the multiple imputa- Haematology, University (NHS) and free for all patients irrespective of tion model included all variables for those being Hospitals of Leicester NHS Trust, Leicester, UK socioeconomic status at the point of use. Private imputed (for further details, see Supplementary William Jones care for those hospitalised with COVID-19 does information). To investigate the effects of multi- Business Intelligence not exist in our region– therefore, our data pro- ple imputation on the results, we conducted a Unit, University Hospitals of Leicester NHS Trust, vide complete coverage of the Leicestershire area. sensitivity analysis using only complete cases. All Leicester, UK Disease severity scoring for patients in the UK is analyses were conducted using Stata (StataCorp. Nigel J. Brunskill Department of guided by the National Early Warning Score 2019). Values of p < 0.05 were considered statis- Cardiovascular Sciences, (NEWS) 2 alert system. Outpatient care is also tically significant. University of Leicester, Leicester, UK provided by NHS hospitals. Department of Nephrology, Leicester General Hospital, We defined a case of COVID-19 as an adult Results Leicester, UK patient with a positive molecular test for SARS- Table 1 shows a description of the cohort strati- Pranab Haldar Department of Respiratory CoV-2 on nasopharyngeal swab. The first wave fied by pandemic wave. A total of 1763 patients Sciences, University of cohort included all cases between 1 March 2020 were included in the final analysis, 956 (54.2%) Leicester, Leicester, UK and data extraction on 28 April 2020. The sec- from the first wave and 807 (45.8%) from the sec- Department of Respiratory Medicine, University ond wave cohort included all cases between 1 ond wave. There were no significant demo- Hospitals of Leicester NHS September 2020 and data extraction on 15 graphic/comorbidity differences between the first Trust, Leicester, UK NIHR Leicester Biomedical November 2020. and second wave cohorts. Research Centre, Leicester, UK Testing for SARS-CoV-2 was based on the pres- Markers of COVID-19 severity and duration of *Joint first authors/ contributed equally ence of symptoms during the first wave; however, hospital stay stratified by ethnicity are shown in †Joint senior authors a nationwide policy of universal testing for all Table 2. Admission NEWS was significantly emergency admissions was adopted from 27 April higher in the first wave compared with the second 2020. Molecular testing was made available for wave in those of White and South Asian ethnicity. hospitalised patients since the start of the first The proportion of deaths after diagnosis with cases of COVID-19 in the United Kingdom, on COVID-19 was higher in the first wave than in 27 January 2020. the second wave. This was observed in White and South Asian patients (26.1% versus 10.8%, We extracted data from the electronic hospital p < 0.001 and 22.1% versus 10.9%, p = 0.001, record concerning age, sex, self-reported ethnic- respectively) but not in Black patients (16.3% ity, postcode, comorbidities (see Supplemental versus 15.4%, p = 0.922). When those who died Table 1), NEWS 2 on admission, admission/dis- and those who were not discharged at the point of charge date, highest supplementary oxygen flow data extraction are excluded, median duration of rate during admission, dexamethasone use, hospital stay was longer in the second wave com- requirement for mechanical ventilation and date pared with the first (4 days, IQR 1–10 versus of death. Ethnicity was categorised as White, 5 days IQR 1–9.5, p = 0.016). South Asian, Black and Other (see Supplemental Table 2). Postcode was used to derive Index of Supplemental Table 3 shows Cox regression for Multiple Deprivation (IMD). survival to 30 days in the whole cohort. Follow-up time (time in days between diagnosis and death Continuous variables were summarised as median or data extraction) was significantly shorter in the [interquartile range (IQR)] and categorical second wave compared with the first [14 (6–26) 2 journals.sagepub.com/home/tai CA Martin, D Pan et al. Table 1. Demographic and comorbidity characteristics of first and second wave patients. Variable Total First Wave Second Wave n = 1763 (1 March–28 April) (1 September–15 November) n = 956 n = 807 Age in years, med (IQR) 66 (52–79) 66 (52–78.5) 67 (51–79) Sex, n (%) Female 759 (43.0) 425 (44.5) 334 (41.4) Male 1004 (57.0) 531 (55.5) 473 (58.6) Ethnicity, n (%) White 1096 (62.2) 605 (63.3) 491 (60.8) South Asian 501 (28.4) 263 (27.5) 238 (29.5) Black 69 (3.9) 43 (4.5) 26 (3.2) Other 97 (5.5) 45 (4.7) 52 (6.5) IMD Quintile, n (%) 1 (most deprived) 329 (18.6) 186 (19.5) 143 (17.7) 2 444 (25.2) 254 (26.6) 190 (23.5) 3 308 (17.5) 150 (15.7) 158 (19.6) 4 355 (20.1) 187 (19.6) 168 (20.8) 5 (least deprived) 314 (17.8) 178 (18.5) 136 (16.9) Missing 13 (0.8) 1 (0.1) 12 (1.5) Comorbidity type, n (%) Hypertension 525 (29.8) 277 (29.0) 248 (30.7) Other cardiovascular 194 (11.0) 94 (9.8) 100 (12.4) Cerebrovascular 93 (5.3) 52 (5.4) 41 (5.1) Respiratory 242 (13.7) 133 (13.9) 109 (13.5) Diabetes 301 (17.0) 167 (17.5) 134 (16.6) Comorbidity number, n (%) 0 881 (50.0) 466 (48.7) 415 (51.4) 1 325 (18.4) 193 (20.2) 132 (16.4) 557 (31.6) 297 (31.1) 260 (32.2) ⩾2 IMD, Index of Multiple Deprivation; IQR, interquartile range; med, median versus 19 (9–27), p < 0.0001]. Factors associated first wave, those in the second wave had around with increased risk of 30-day mortality were half the risk of 30-day mortality [adjusted hazard increasing age, male sex, presence of diabetes and ratio (aHR) 0.55, 95% confidence interval (CI) admission NEWS. Compared with those in the 0.40–0.75]. journals.sagepub.com/home/tai 3 Therapeutic Advances in Infectious Disease 9 4 journals.sagepub.com/home/tai Table 2. Markers of severity and outcome data stratified by pandemic wave and ethnicity. Variable First wave (n = 956) Second wave (n = 807) Total White South Asian Black Other Total White South Asian Black Other NEWS score on 3 (1–4.5)* 2 (1–4)* 3 (1–5)* 3 (1–5) 3 (1–7) 2 (0–4)* 2 (0–4)* 2 (0–4)* 2 (0–4) 0 (1–4) admission, med (IQR) Missing, n (%) 20 (2.1) 10 (1.7) 5 (1.9) 1 (2.3) 4 (8.9) 4 (0.5) 1 (0.2) 3 (1.3) 0 (0.0) 0 (0.0) Maximum oxygen flow rate 2 (0–10)* 3 (0–10)* 2 (0–8) 2 (0–4) 0 (0–4) 2 (0–6)* 2 (0–6)* 2 (0–8) 0 (0–8) 0 (0–3) received during admission (L/min), med (IQR) Not mechanically ventilated 886 (92.7%) 571 (94.4%) 238 (90.5%) 39 (90.7%) 38 (84.4%) 778 (96.5%) 479 (97.6%) 226 (95.0%) 25 (96.2%) 48 (92.3%) † † † † Mechanically ventilated 70 (7.3%) 34 (5.6%) 25 (9.5%) 4 (9.3%) 7 (15.6%) 29 (3.6%) 12 (2.4%) 12 (5.0%) 1 (3.9%) 4 (7.7%) Survived 728 (76.2%) 447 (73.9%) 205 (78.0%) 36 (83.7%) 40 (88.9%) 723 (89.6%) 438 (89.2%) 212 (89.1%) 22 (84.6%) 51 (98.1%) † † † † † † Died 228 (23.9%) 158 (26.1%) 58 (22.1%) 7 (16.3%) 5 (11.1%) 84 (10.4%) 53 (10.8%) 26 (10.9%) 4 (15.4%) 1 (1.9%) Excluding those not discharged or dead at the time of data extraction Variable First wave (n = 794) Second wave (n = 576) Not mechanically ventilated 760 (95.7%) 482 (96.2%) 211 (95.1%) 36 (94.7%) 31 (93.9%) 562 (97.6%) 332 (97.9%) 180 (97.3%) 19 (95.0%) 31 (96.9%) Mechanically ventilated 34 (4.3%) 19 (3.8%) 11 (5.0%) 2 (5.3%) 2 (6.1%) 14 (2.4%) 7 (2.1%) 5 (2.7%) 1 (5.0%) 1 (3.1%) Survived 566 (71.3%) 343 (68.5%) 164 (73.9%) 31 (81.6%) 28 (84.9%) 492 (85.4%) 286 (84.4%) 159 (86.0%) 16 (80.0%) 31 (96.9%) † † † † † † Died 228 (28.7%) 158 (31.5%) 58 (26.1%) 7 (18.4%) 5 (15.2%) 84 (14.6%) 53 (15.6%) 26 (14.1%) 4 (20.0%) 1 (3.1%) Excluding those who died and those not discharged at the time of data extraction Variable First wave (n = 566) Second wave (n = 492) Length of stay in days, med 4 (1–10)* 5 (1–10) 3 (1–6)* 3 (0–7) 1 (0–6.5) 5 (1–9.5)* 5 (2–11) 4 (1–9)* 4 (1.5–6) 3 (1–8) (IQR) IQR, interquartile range; NEWS, National Early Warning Score. Wilcoxon rank-sum p < 0.05. Chi-square p < 0.05. CA Martin, D Pan et al. Table 3. Multivariable Cox proportional hazards model for survival at 30 days from COVID-19 diagnosis in the first and second pandemic waves. Variable First wave (n = 956) Second wave (n = 807) aHR (95% CI) p value aHR (95% CI) p value Age 1.05 (1.04–1.07) 1.07 (1.05–1.10) <0.001 <0.001 Sex Female Ref – Ref – Male 1.50 (1.13–2.00) 0.005 2.22 (1.29–3.81) 0.004 Ethnicity White Ref – Ref – South Asian 1.04 (0.73–1.49) 0.81 0.90 (0.52–1.56) 0.71 Black 0.88 (0.38–2.04) 0.76 4.73 (1.56–14.34) 0.006 Other 0.71 (0.28–1.78) 0.47 0.50 (0.06–3.87) 0.51 IMD quintile 1 (least deprived) Ref – Ref – 2 1.20 (0.79–1.83) 0.38 1.00 (0.49–2.05) 1.00 3 0.98 (0.60–1.59) 0.92 1.20 (0.58–2.49) 0.62 4 0.89 (0.56–1.41) 0.62 0.59 (0.27–1.29) 0.19 5 (most deprived) 0.91 (0.58–1.43) 0.69 0.81 (0.35–1.86) 0.62 Comorbidity type Hypertension 0.84 (0.58–1.22) 0.36 1.96 (1.00–3.82) 0.05 Other cardiovascular 0.78 (0.51–1.21) 0.26 1.19 (0.68–2.09) 0.55 Stroke 1.25 (0.76–2.06) 0.38 0.71 (0.24–2.06) 0.53 Respiratory disease 1.00 (0.64–1.58) 0.99 1.49 (0.80–2.75) 0.21 Diabetes 1.43 (0.99–2.08) 0.06 2.03 (1.15–3.59) 0.02 Comorbidity number 0 Ref – Ref – 1 1.46 (0.95–2.24) 0.08 2.58 (1.07–6.23) 0.03 1.24 (0.69–2.22) 0.48 1.23 (0.41–3.68) 0.71 ⩾2 NEWS score on admission 1.28 (1.23–1.33) 1.34 (1.23–1.46) <0.001 <0.001 Treatment Did not receive dexamethasone Ref – Ref – Received dexamethasone 0.14 (0.02–0.99) 0.049 1.55 (0.95–2.51) 0.08 aHR, adjusted hazard ratio; CI, confidence interval; IMD, Index of Multiple Deprivation; NEWS, National Early Warning Score. journals.sagepub.com/home/tai 5 Therapeutic Advances in Infectious Disease 9 Separate multivariable Cox regression models for COVID-19 (dexamethasone). Clearly a reduc- survival to 30 days in the first and second waves tion in COVID-19 mortality is desirable; however, are shown in Table 3. In both pandemic waves, increased survival from COVID-19 in the second age, male sex and admission NEWS score are sig- wave may contribute to pressure on the healthcare nificantly associated with a higher risk of 30-day system through increased hospital bed occupancy. mortality. In the first wave, ethnicity did not Indeed our analysis shows that duration of hospital impact upon 30-day mortality; however, in the stay was longer in the second wave than in the first. second wave, as compared with White patients, Nonpharmaceutical interventions, such as man- those of Black ethnicity were almost 5 times as dating masks and lockdowns, may have also likely to die within 30 days (aHR 4.72, 95% CI affected the number of patients infected and thus 1.56–14.43). When the waves were analysed in turn, the number of deaths, but the city of together, the interaction between pandemic wave Leicester remained in full lockdown (stay-at-home and Black ethnicity was not significant (aHR except for essential purchases, remote work, one 3.25, 95% CI 0.87–12.07, p = 0.079). When only exercise per day, cancellation of public gatherings complete cases are analysed, significant findings and social events and no travel abroad) from 23 do not change. March 2020 to the end of the study. We observed that mortality rates in the second Discussion pandemic wave may not be equal across ethnic In this study within an ethnically diverse cohort, groups. In our second wave cohort, those of Black we found a number of novel observations. First, ethnicity were at higher risk of 30-day mortality there were no significant differences in demo- than White individuals (an effect not seen in the graphic characteristics between patients in the first wave cohort). These findings are opposite to first and second COVID-19 pandemic waves and a national UK study by Mathur et al., which that markers of severe COVID-19 (NEWS, found that compared with the first wave, risks for requirement for mechanical ventilation and death were attenuated for those from Black ethnic COVID-19-associated mortality) were lower in groups compared with White ethnic groups. the second wave of the pandemic compared with Ethnicity is a social determinant of health. the first. We found those of Black ethnicity were Therefore, it is likely that the differences in risks at higher risk of 30-day mortality than those of seen in our study reflect the number of infections White ethnicity in the second wave. that occurred in the local community and conse- quently, the number of people who present to COVID-19 case fatality rates have fallen in the sec- hospital. A disproportionate number of infections ond wave in 43 of the 53 countries with the highest in those of Black ethnicity may reflect socioeco- total COVID-19-related deaths. Foremost among nomic factors, such as occupation, which prevent the explanations for this observation is the them from being able to stay at home on a second increased availability of testing in the second wave lockdown. Leicester is an especially ethnically which may increase the number of paucisympto- diverse part of the United Kingdom, where matic/asymptomatic individuals who are tested. In approximately 30% of the population is born out- our study, the change in SARS-CoV-2 testing pol- side of Europe and North America, and individu- icy from symptom-driven testing to universal test- als from the Indian subcontinent alone make up ing would be expected to pick up bystander 15% of the population. However, we recom- infection in those attending hospital for reasons mend cautious interpretation of this finding, due unrelated to COVID-19 and could generate bias to the small number of Black patients in our sam- towards a less symptomatic sample in the second ple and the nonsignificant interaction between wave. Other suggested mechanisms include the ethnicity and pandemic wave. Nonetheless, given changing demographic profile of COVID-19 cases the higher risk of SARS-CoV-2 infection and over time, with a smaller proportion of elderly adverse outcome from COVID-19 in ethnic patients (a group at high risk of adverse outcome minority groups compared with those of White 1,5,13,14 from COVID-19) in the second pandemic wave ethnicity, this observation requires urgent 8,9 with more people shielding, although we found follow-up by larger studies with a greater propor- no such differences in our dataset. In addition, in tion of Black participants and emphasises the contrast to the early part of the first wave, there is importance of targeted interventions such as tai- an effective pharmacological treatment for severe lored public health messaging in these groups. 6 journals.sagepub.com/home/tai CA Martin, D Pan et al. This study has limitations. Data are from a single Methodology; Project administration; Writing – centre and our data do not allow us to control for original draft; Writing – review & editing the change in testing policy between waves. We Daniel Pan: Conceptualisation; Data curation; found a shorter median follow-up time in second Formal analysis; Investigation; Methodology; wave patients. This is likely due to the fact that Writing – original draft; Writing – review & editing while the first wave data capture peak COVID-19 hospital admissions in early April with data George Hills: Conceptualisation; Data curation; extraction a month later, COVID-19 admissions Investigation; Writing – review & editing continued to increase after data extraction for the Deborah Modha: Conceptualisation; Writing – second wave, meaning that a greater proportion review & editing of the second wave cohort were admitted close to the point of data extraction when compared with Prashanth Patel: Conceptualisation; Data cura- the first wave. However, we conducted an explor- tion; Investigation; Writing – review & editing atory analysis excluding those admitted after the Laura Gray: Data curation; Formal analysis; first wave ‘peak’ and still demonstrated a signifi- Writing – review & editing cant protective effect of being admitted during the second wave compared with the first. Mortality David Jenkins: Conceptualisation; Writing – rates and severity of COVID-19 may change in review & editing subsequent waves of the pandemic, particularly Linda Barton: Investigation; Writing – review & with the emergence of the new variants of SARS- editing CoV-2. We only collected routinely available var- iables that were recorded within our clinical William Jones: Data curation; Writing – review systems and as such, we do not have granular & editing information – such as occupation – which may Nigel Brunskill: Methodology; Writing – review have affected their risk of infection. No patients in & editing the United Kingdom were vaccinated at the time of study as part of national policy – these findings Pranab Haldar: Conceptualisation; Writing – may be different now that a significant proportion review & editing of the UK population has been vaccinated. The Kamlesh Khunti: Conceptualisation; Writing – emergence of novel variants, especially during the review & editing period of the second wave, may have had an impact on our study outcomes – however, Manish Pareek: Conceptualisation; Formal genomic sequencing is not routinely performed in analysis; Methodology; Project administration; all our hospitals. Despite these limitations, our Supervision; Writing – review & editing findings, particularly those related to ethnicity, must be investigated by larger studies to ensure that the inequalities of the first wave are not Conflict of interest statement allowed to widen. The authors declared the following potential con- flicts of interest with respect to the research, Acknowledgements authorship and/or publication of this article: KK CAM and DP are National Institute for Health is the Director of the University of Leicester Research (NIHR) academic clinical fellows. LG Centre for Black Minority Ethnic Health, Trustee and KK are supported by NIHR Applied Research of the South Asian Health Foundation and Chair Collaboration East Midlands (ARC EM). KK of the Ethnicity Subgroup of SAGE. MP reports and MP are supported by the NIHR Leicester grants and personal fees from Gilead Sciences Biomedical Research Centre (BRC). The views and personal fees from QIAGEN, outside the expressed are those of the author(s) and not nec- submitted work. essarily those of the NIHR, National Health Service (NHS) or the Department of Health and Funding Social Care. The authors disclosed receipt of the following financial support for the research, authorship Author contributions and/or publication of this article: This work was Christopher Martin: Conceptualisation; Data supported by the National Institute for Health curation; Formal analysis; Investigation; Research (NIHR). The funders had no role in journals.sagepub.com/home/tai 7 Therapeutic Advances in Infectious Disease 9 ethnicity and household size: results from an design, data collection and analysis, decision to observational cohort study. EClinicalMedicine publish or preparation of the article. MP is sup- 2020; 25: 100466. ported by an NIHR Development and Skills Enhancement Award and funding from UKRI/ 6. Thomas R. All hospital emergency patients to MRC (MR/V027549/1). be tested for coronavirus, https://www.hsj.co.uk/ coronavirus/all-hospital-emergency-patients-to- be-tested-for-coronavirus/7027526.article (2020, Ethics statement accessed 6 January 2021). Ethical approval was not required after consulta- tion with the National Health Service (NHS) 7. Royal College of Physicians. 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