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      Ethnic inequalities in COVID-19 infection, hospitalisation, intensive care admission, and death: a global systematic review and meta-analysis of over 200 million study participants

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          Abstract

          Background

          COVID-19 has exacerbated existing ethnic inequalities in health. Little is known about whether inequalities in severe disease and deaths, observed globally among minoritised ethnic groups, relates to greater infection risk, poorer prognosis, or both. We analysed global data on COVID-19 clinical outcomes examining inequalities between people from minoritised ethnic groups compared to the ethnic majority group.

          Methods

          Databases (MEDLINE, EMBASE, EMCARE, CINAHL, Cochrane Library) were searched from 1st December 2019 to 3rd October 2022, for studies reporting original clinical data for COVID-19 outcomes disaggregated by ethnicity: infection, hospitalisation, intensive care unit (ICU) admission, and mortality. We assessed inequalities in incidence and prognosis using random-effects meta-analyses, with Grading of Recommendations Assessment, Development, and Evaluation (GRADE) use to assess certainty of findings. Meta-regressions explored the impact of region and time-frame (vaccine roll-out) on heterogeneity. PROSPERO: CRD42021284981.

          Findings

          77 studies comprising over 200,000,000 participants were included. Compared with White majority populations, we observed an increased risk of testing positive for infection for people from Black (adjusted Risk Ratio [aRR]:1.78, 95% CI:1.59–1.99, I 2 = 99.1), South Asian (aRR:3.00, 95% CI:1.59–5.66, I 2 = 99.1), Mixed (aRR:1.64, 95% CI:1.02–1.67, I 2 = 93.2) and Other ethnic groups (aRR:1.36, 95% CI:1.01–1.82, I 2 = 85.6). Black, Hispanic, and South Asian people were more likely to be seropositive. Among population-based studies, Black and Hispanic ethnic groups and Indigenous peoples had an increased risk of hospitalisation; Black, Hispanic, South Asian, East Asian and Mixed ethnic groups and Indigenous peoples had an increased risk of ICU admission. Mortality risk was increased for Hispanic, Mixed, and Indigenous groups. Smaller differences were seen for prognosis following infection. Following hospitalisation, South Asian, East Asian, Black and Mixed ethnic groups had an increased risk of ICU admission, and mortality risk was greater in Mixed ethnic groups. Certainty of evidence ranged from very low to moderate.

          Interpretation

          Our study suggests that systematic ethnic inequalities in COVID-19 health outcomes exist, with large differences in exposure risk and some differences in prognosis following hospitalisation. Response and recovery interventions must focus on tackling drivers of ethnic inequalities which increase exposure risk and vulnerabilities to severe disease, including structural racism and racial discrimination.

          Funding

          doi 10.13039/501100000269, ESRC; :ES/W000849/1.

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          Most cited references136

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          Quantifying heterogeneity in a meta-analysis.

          The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
            • Record: found
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            Is Open Access

            Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline

            In systematic reviews that lack data amenable to meta-analysis, alternative synthesis methods are commonly used, but these methods are rarely reported. This lack of transparency in the methods can cast doubt on the validity of the review findings. The Synthesis Without Meta-analysis (SWiM) guideline has been developed to guide clear reporting in reviews of interventions in which alternative synthesis methods to meta-analysis of effect estimates are used. This article describes the development of the SWiM guideline for the synthesis of quantitative data of intervention effects and presents the nine SWiM reporting items with accompanying explanations and examples.
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              The COVID-19 pandemic and health inequalities

              This essay examines the implications of the COVID-19 pandemic for health inequalities. It outlines historical and contemporary evidence of inequalities in pandemics—drawing on international research into the Spanish influenza pandemic of 1918, the H1N1 outbreak of 2009 and the emerging international estimates of socio-economic, ethnic and geographical inequalities in COVID-19 infection and mortality rates. It then examines how these inequalities in COVID-19 are related to existing inequalities in chronic diseases and the social determinants of health, arguing that we are experiencing a syndemic pandemic. It then explores the potential consequences for health inequalities of the lockdown measures implemented internationally as a response to the COVID-19 pandemic, focusing on the likely unequal impacts of the economic crisis. The essay concludes by reflecting on the longer-term public health policy responses needed to ensure that the COVID-19 pandemic does not increase health inequalities for future generations.

                Author and article information

                Journal
                eClinicalMedicine
                EClinicalMedicine
                eClinicalMedicine
                The Authors. Published by Elsevier Ltd.
                2589-5370
                6 March 2023
                6 March 2023
                : 101877
                Affiliations
                [a ]School of Social Sciences, University of Manchester, United Kingdom
                [b ]Department of Respiratory Sciences, University of Leicester, United Kingdom
                [c ]Department of Infection and HIV Medicine, University Hospitals Leicester NHS Trust, United Kingdom
                [d ]Li Ka Shing Centre for Health Information and Discovery, Oxford Big Data Institute, University of Oxford, United Kingdom
                [e ]NIHR Leicester Biomedical Research Centre, United Kingdom
                [f ]Department of Global Health and Social Medicine, King's College London, United Kingdom
                [g ]Department of Cardiovascular Sciences, University of Leicester, United Kingdom
                [h ]MRC/CSO Social & Public Health Sciences Unit, University of Glasgow, United Kingdom
                [i ]University Hospitals of Leicester, Education Centre Library, Glenfield Hospital and Leicester Royal Infirmary, United Kingdom
                [j ]Department of Health Sciences, University of Leicester, United Kingdom
                [k ]Division of Epidemiology and Public Health, School of Medicine, University of Nottingham, United Kingdom
                Author notes
                []Corresponding author. School of Social Sciences, Faculty of Humanities, University of Manchester, Oxford Road, Manchester, M13, 9PL, UK.
                [l]

                Authors contributed equally.

                Article
                S2589-5370(23)00054-8 101877
                10.1016/j.eclinm.2023.101877
                9986034
                36969795
                9b1dd5b4-e6f2-414a-b9c1-453972b5447e
                © 2023 The Authors

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 8 August 2022
                : 2 February 2023
                : 2 February 2023
                Categories
                Articles

                meta-analysis,ethnicity,covid-19,sars-cov-2,systematic review,prognosis

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